Accurate indoor localization is a crucial enabling technology for many robotic applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) localization technology, in particular, has been shown to provide robust, high-resolution, and obstacle-penetrating ranging measurements. Nonetheless, UWB measurements are still corrupted by non-line-of-sight (NLOS) communication and spatially-varying biases due to doughnut-shaped antenna radiation pattern. In our recent work, we present a lightweight, two-step measurement correction method to improve the performance of both TWR and TDoA-based UWB localization.  We integrate our method into the Extended Kalman Filter (EKF) onboard a Crazyflie and demonstrate a closed-loop position estimation performance with ~20cm root-mean-square (RMS) error.

A stylized depiction of our UWB indoor localization system and the schematics of the proposed estimation framework.


UWB measurement errors can be separated into two groups: (1) systematic bias caused by limitations in the UWB antenna pattern and (2) spurious measurements due to NLOS and multi-path propagation. We propose a two-step UWB bias correction approach exploiting machine learning (to address(1)) and statistical testing (to address (2)). The data-driven nature of our approach makes it agnostic to the origin of the measurement errors it corrects. 

(1) Neural Network Bias Correction

The doughnut-shaped antenna radiation pattern causes the relative poses of anchors and tags to have a noticeable impact on the received signal power, which leads to systematic, predictable biases.  To empirically demonstrate the systematic measurement errors resulting from varying the relative pose between anchors and tags, we placed two DWM1000 UWB anchors at a distance of 4m and collected both TWR and TDoA UWB range measurements for the UWB tag mounted on top of a Crazyflie spinning around its own z-axis.

Left: schematics of the ranges (∆p’s), azimuth (α’s) and elevation angles (β’s) defining the relative poses of tag T and anchors A0, A1 when collecting the systematic bias measurements. Right: the neural network’s inferred bias (in red) with respect to the tag’s varying azimuth angle towards anchor T0, αT0, plotted against the UWB raw measurements.

We choose to leverage the nonlinear representation power of neural networks to learn the systematic bias which only depends on anchor-tag relative poses. Considering the limited onboard computation power, we select a fully connected neural network with 50 neurons in each of two layers with ReLU activation. To represent the relative pose between the UWB tag and anchors, we select the relative distance ∆p and roll, pitch, and yaw angles of the quadcopter as the input features x for the network. As we used fixed anchors, we do not include their poses as inputs (this level of generalization is left for future work). Given sufficient training data, the spatially-varying measurement bias can be described by a nonlinear function b=f(x) captured by the trained neural network.

(2) Outlier (Spurious Measurements) Rejection

Besides our learning-based bias correction, we use a quadcopter’s dynamic model to filter inconsistent UWB range measurements. Given the estimated velocity v and maximum acceleration amax, we can compute the maximum distance dmax a quadcopter can cover during time ∆t. Based on this information, we can reject unattainable measurements before fusing them into the EKF by comparing the measurement innovation with dmax

Moreover, we use a statistical hypothesis test to further classify potential outlier measurements. Since the measurement innovation vector is assumed to be distributed according to a multivariate Gaussian distribution, the normalized sum of squares of its values should follow a Chi-square distribution. We use the Chi-square hypothesis test to determine whether a measurement innovation is likely coming from this distribution.

UWB measurement bias f (x) prediction performance of the trained neural network (in red) compared to the actual measurement errors (blue dots) as well as the role of model-based filtering (purple dots) and statistical validation (orange dots) in rejecting outlier measurement innovations (teal dots) during a 60” flight experiment.

Data Collection and Training

We use a Crazyflie 2.0 quadcopter and the Loco Positioning System (LPS)’s UWB DW1000 modules as our research platforms. Our calibration approach runs on the Crazyflie STM32 microcontroller within the FreeRTOS real-time operating system. We equipped a cuboid flying arena with 8 UWB anchors, one for each vertex. The anchor positions were measured using a Leica total station theodolite.

Left: three-dimensional plot of our flight arena showing the positions and poses of the eight UWB DW1000 anchors (each facing towards its own x-axis, i.e., the red versor). Right: two of the training trajectories we flew to collect the samples that we used to train our neural network-based bias estimator

For all experiments, the ground truth position of the Crazyflie was provided by 10 Vicon cameras. The neural network was trained using PyTorch. To perform inference on the Crazyflie’s microcontroller, we re-use PyTorch’s trained weights in a plain C re-implementation. Since the DW1000 modules in the LPS provide UWB measurements every 5ms, the neural network inference runs at 200Hz during flight as well. Our outlier rejection method is also implemented in plain C and merged with the onboard EKF.

Close-loop Position Estimation Performance

We demonstrate the position estimation and close-loop performance of the proposed methods by flying a Crazyflie quadcopter along planar and non-planar circular trajectories (which were not among the trajectories used for training). A comparison between the estimation error of (A) the UWB localization estimate enhanced with outlier rejections and (B) the estimated enhanced with both outlier rejection and neural network bias compensation is conducted in our experiments for both TWR and TDoA2 modes. We repeated all of our experiments 10 times with a target velocity of 0.375m/s. The quadcopter trajectories during these flight tests are displayed in the following plots.  

Flight paths and the tracking performance of our approach with (in blue) and without (in orange) the neural network bias correction for two reference trajectories (planar and non-planar circular orbits) and both UWB modes (TWR and TDoA).

The distributions of the RMS estimation errors are summarized into a box plot. TWR-based ranging results in better localization performance than TDoA. However, we observe that, with our neural network bias compensation, the average RMS error of TDoA localization is around 0.21m, which is comparable to that of TWR-based localization (~0.19m). Thanks to the neural network bias compensation, the average reduction in the RMS error is ~18.5% and 48% for TWR and TDoA, respectively. Most notably, this result suggests that bias compensation might help closing the performance gap between TWR- and TDoA-based localization.

Root mean square error (RMSE) of the quadcopter position estimate before (in orange) and after (in blue) the neural network calibration step for both TWR and TDoA ranging modes. Each pair of box plots refers to a planar reference trajectory (left of each pair) and a reference trajectory with varying z (right of each pair), showing a greater performance enhancement for the latter.


In this work, we presented a two-step methodology to improve UWB localization—for both TWR- and TDoA-based measurements. We used a lightweight neural network to model and compensate for pose-dependent and spatially-varying biases and an outlier rejection mechanism to filter spurious measurements. Through several real world flight experiments tracking different trajectories, we showed that we are able to improve localization accuracy for both TWR and TDoA, granting safer indoor flight. In our future work, we will include the anchors’ pose information to allow our method to further generalize to previously unobserved indoor environments, with different anchor configurations.


The authors are with the Dynamic Systems Lab, Institute for Aerospace Studies, University of Toronto, Canada, and affiliated with the Vector Institute for Artificial Intelligence in Toronto.

Feel free to contact us if you have any questions or ideas: Please cite this as:

  title={Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots},
  author={Wenda Zhao and Abhishek Goudar and Jacopo Panerati and Angela P. Schoellig},
  journal={arXiv preprint arXiv:2003.09371},

The Lighthouse V2 implementation has been simmering away for a long time in the Bitcraze kitchen and in this blog post we will give you an update on the current status and what is remaining for a full release of this tasty dish.

Crazyflie 2.1 and Lighthouse V2 base station

We believe we have solved most of the major technical hurdles (last famous words) on the way to a working implementation that uses Lighthouse V2 base stations for positioning, now it is mostly work to implement the functionality that is remaining. As described in this post we now have a new FPGA binary that has the ability to decode both V1 and V2 base stations, and this was a major step forward. This new binary is used in the Crazyflie firmware master branch, and if the Lighthouse deck is used with the latest Crazyflie firmware, the new FPGA binary will automatically be flashed to the deck.

What has changed?

The new FPGA binary uses a different UART protocol to communicate with the Crazyflie. This protocol has been implemented in the firmware and hopefully there is no functional difference compared to the previous FPGA binary when using Lighthouse V1 base stations.

We have added a first version of Lighthouse V2 base station decoding, but it is still a bit limited. As a start we decided to “emulate” V1 base stations to be able to reuse as much of the existing code as possible. For now we support only 2 base stations and they must use channel 1 and 2 (used to be called modes). The V2 angles are transformed into V1 angles and fed into the old positioning logic and are handled exactly the same way as before. Even though this works, it is not the optimal solution and we hope to be able to refine it later on.

We have also written a python script to estimate base station geometry (positions and orientation) using the Lighthouse deck. This removed the requirement to use software from Steam which should simplify the set up process. Please see the (still limited) documentation. Note that this calibration method only supports the basestation V1… for now!

There is a lot of code that has been modified and the FPGA implementation is completely new, it is not unlikely that there is functionality that is unstable or broken, or configurations that are not supported. If you happen to notice any bugs, please let us know!

What is remaining?

The functional areas that needs to be implemented or cleaned up before we leave the Early Access stage is the following:

Calibration data

The calibration data is embedded in the modulated light from the base stations and describes imperfections from the manufacturing process for each individual. This data is not read yet for V2 and will increase the precision when available.

Support for more than 2 base stations

Lighthouse V2 base stations are designed for systems with more than 2 base stations. The Crazyflie firmware needs to be extended for this functionality to work, including handling of geometry data, logging, memory management and some other bits and pieces.

Native V2 positioning

The angles from the V2 base station should be fed directly into the kalman filter for positioning, instead of first being transformed into V1 angles. This will increase robustness and reduce data loss.

Client support

We want to add a tab in the python client where a Lighthouse system can be monitored, configured and managed. It should, for instance enable the user to configure and visualize base station geometry.

FPGA binaray management

Currently the FPGA binary is included in the Crazyflie firmware and it is automatically uploaded to the deck when booted. This is not a viable long term solution and we hope to be able to find a more generic way of handling deck binaries.


As can be seen, there is still quite some work to be done before the Lighthouse V2 stew is ready to be served, but we are definitely starting to smell some nice flavours from the kitchen!

Finally a view from Kristoffer’s home lab, currently in the summer house. Three base stations are set up as a Fun Friday hack to see what it would take to use more than 2. Luckily it did not take too much time to get this to work :-)

3 Lighthouse V2 base stations

In this blog-post we wanted to give you guys an overview of our running projects and a general update of the status of things! We got settled in our home-labs and are working on many projects in parallel. There are a lot of development happening at the moment, but the general feeling is that we do miss working with each other at our office! With our daily slack Bitcraze sync meetings and virtual fikapause (Swedish for coffee breaks), we try to substitute what we can. In the mean time, we are going on a roll with finishing all our goals we have set at our latest quarterly meeting, so here you can read about those developments.


Crazyflie with AI-deck

The last time we gave an update about the AI-deck was in this blog post and in the final post of our intern Zhouxin. Building on his work, we are now refocusing on getting the AI-deck ready for early release. The last hurdle is mostly software wise on which we are considering several approaches together with the manufacturer of the Gap8 chip Greenwaves technologies. Currently we are preparing small testing functions as examples of the different elements of the AI-deck in our repo, which are all still in a very primarily phase.

Even though we still need some time to finalize the AI-deck’s early release, we will consider sending an early version of the AI-deck if you are willing to provide feedback while working with it. Please fill in the form and we will get back to you.


We have made quite some progress on the development for the lighthouse V2. Kristoffer has been working hard from his homelab to get a seamless integration of both V1 and V2 in our firmware (check out this github issue for updates). Currently it is still very untested and very much in progress, however we do have a little preview for you to enjoy.

Crazyflie with LH basestation v2


Right now, we are also doing a lot of revamping of the large web of documentation. Unfortunately this is a lot of work! As you noticed by now, we have added overview pages to guide the reader to the right information. We also have moved the tutorials to another part of the menu to avoid clutter on our website. In general we try to go through the repository docs to see if there is any information missing or outdated, however please let us know if you have encountered an error in any description or are missing crucial elements.

Our latest task is revamping the product pages as well, by putting all the necessary information about the hardware in just one place. Also, we are planning to make (video) tutorials soon about many elements of the Crazyflie and how to work with it. More about that later!

Production and Shipment

Production at our manufacturers in China are slowly starting up again. Although it is not yet back at full force, it does enable us to already start ordering to replenish our stock and to get started with finishing our test rigs. Moreover, we are also negotiating to resolve the propeller issue we mentioned earlier, but there is no update on that so far.

As mentioned in this blogpost, we are still shipping orders about twice a week. Both DHL and Fedex are functioning as normal, but we do notice that there is a delay of a few extra days on some deliveries. Please keep that in mind when ordering at our webshop.

Many people in the world have now settled in the reality of working from home. We have also taken precautions ourselves by not go to our office as normal and only ship out packages a few times per week instead of every day (see this blogpost). This also means that we do not have full access to our lab with all our equipment and positioning systems in our big 10 x 10 meter flight lab at the office. In this blogpost we will show how we manage to keep on developing and flying, even in the current situation.

Crazyflie flying in a kitchen with the lighthouse deck

In(light)house positioning

Currently we started to use the Lighthouse positioning system to setup up the remote home lab at our houses. As of recent additions to the Crazyflie firmware, it has been made easy to get the geometry data from the base station. Now the only items we need for indoor flight are just two (or only one) lighthouse basestations V1’s and a Crazyflie, and that is it! There is no need for an HTC Vive headset or hub, or third-party software like SteamVR and the setup is finished in 2 minutes! Check out the new documentation here if you want to know more about the new setup of the lighthouse positioning system.

Also, we recently got a very primarily version of the lighthouse V2 working (see here) and we of course want to keep the momentum going! We will be working on full compatibility from our homes so stay tuned. For now, see this video of the Crazyflie flying with just a single base-station, taken from one of our team-member’s home lab.

Remote Lecture Hall and Practicals

We were invited by Dario Floreano and Fabrizio Schiano from the EPFL-LIS laboratory to do a lecture for the ‘Aerial Robotics’ Course as part of EPFL’s Master’s program in Robotics. Due to the virus, we had to cancel our trip to go there physically… but luckily we were able to do the lecture remotely anyway!

Screenshots of the lectures

The lecture consists of two parts. In the first hour we mostly explained about the Crazyflie ecosystem, hardware and sensors. In the second hour we focused on how the stabilization module worked, including the controllers and the state estimation. During both sessions, we alternated between the theory slides with actual hands-on demos. The lighthouse positioning system was setup in a kitchens, so that we were able to show full flights and practicals with the Crazyflie. At the end there was also the push-demo with just the flowdeck and multiranger, which didn’t use any external positioning at all.

The lectures can be found below and the documentation has been updated as well with the covered material (see here). Be sure to check out the controller tuning presented in part 2 of the lecture (25:00 – Cascaded PID controller).

Other Home labs

Home lab with Crazyflie

We know that there are currently users that are moving their flight lab from their university or company to their homes to be able to continue their work. We would love to hear about your experience and your home lab! Send us an email with your story to, drop us a message on, or mention us in your Twitter, Linkedin, Facebook or Reddit post. Also, if you want to setup your own home lab and you need any advice or help, please let us know!

We have mentioned the Active Marker deck in an earlier blog post, and are now happy to announce that it has been released and is available in our store.

Crazyflie with Active marker deck

By changing the passive, reflective markers to active, IR-LEDs, it is possible to improve the detection of markers in the cameras. There are two main reasons: the area of the marker is smaller and easier to separate from other markers close by, and the LEDs are emitting light and can be detected further away.

The deck has been developed in collaboration with Qualisys and together with the QTM system, it utilizes their Active Marker technology. An ID is assigned to each marker, and since the identity of each marker can be detected by the MoCap system, it is possible to estimate the full body pose of the Crazyflie without unique marker positions or known starting positions. IDs are easily assigned using the parameter sub system of the Crazyflie.

Even though the deck mainly is intended to be used with Qualisys MoCap systems, the LED markers can also be configured to be on or off which we hope might be useful in other applications as well.

Who knew propellers would be that hard? Already from the very beginning with the Crazyflie 1.0, we had problems with very unbalanced propellers resulting in reduced flight performance. The fix at that time was to manually test and sort out the bad propellers. This worked well until the manufacturer’s injection mould got too worn down and we had to reject a lot of propellers. The manufacturer didn’t want to continue selling us the propellers unless we accepted them all. The hunt for a new manufacturer begun and after trying several, we finally, just in time for the Crazyflie 2.1, found one that could deliver well balanced propellers.

That could have been the end of a happy story but recently we found out that the new propellers tend to break too easily. The root cause seems to be that the center hole is too tight, causing tension in the plastic which makes it more fragile and prone to break.

Typically broken propeller

We don’t fully understand when this started but it looks like it was not that frequent in the beginning when the Crazyflie 2.1 was released and that it has increased from the batch manufactured in the end of July 2019. We don’t have data on how many propellers are bad but our estimation is around 20-40% and it is booth CW and CCW propellers. It also includes the spare part bundles manufactured in the second half of 2019.

Currently out main focus is to fix this propeller manufacturing issue. As soon as we have done so, we will lunch some form of replacement propeller program so that those of you that have gotten many bad propellers can get new ones for free. We don’t have any time estimate right now, and due to the world covid-19 crisis we have a feeling it can take a while. We are very sorry about this!

Insert propeller so it sits firmly, but not further


There is an easy, but not ideal, workaround for this and that is to not push the propeller all the way down on the shaft. Instead stop when force is getting high and it holds the propeller in place. This will prevent the tension in the propeller to become big enough to break.

For those that have ordered after the 16th of March we have fixed the propellers by drilling the center hole slightly larger. This solves the problem for those units until we have fixed the root cause at the manufacturer.

Considering the recent pandemic and the global situation, we at Bitcraze had to think about what will be our next step. Even though everyday life in Sweden is still going on, and all of us are in good health, we know there are a lot of you that are impacted by the CoronaVirus. We wanted to take responsibility for each other, the Swedish health care system, and the rest of our community by actively minimizing our social interactions. The best we can do right now is contributing to stop the disease and preserve our health. 

So for the time being, we all will be working from home. Shipping of orders will be a bit slower (twice every week instead of the current four times), but other than that we hope to still be available as usual. Forums posts, sales and other daily tasks will go on as usual. Hopefully you won’t see the difference! We will still work on providing you the best flying experience we can. 

With the new situation comes new challenges for the team, that we’re trying to make the best of. First of all, we will test a whole new way of working – together but apart! And we hope this will also give us the opportunity to work on some things that otherwise are a little bit left out… But you’ll have to be a little patient to discover these!

If you’re stuck at home and are getting bored, why not fly your Crazyflie and send us a video? We’ll post regularly those on our social media to fight against the current feeling of gloom and potential loneliness associated with confinement!

Our thoughts and well-wishes are with everyone affected by the situation.

This is it. The end of my internship. It feels strange to leave this unique office in a place called Malmö. My time spent here was more than just doing an assignment as part of a MSc. degree with the objective that I would gain working experience and contribute to a company.

My last day at the office of Bitcraze, Arnaud was already on parental leave

My time here gave me so much more. I have learned here a healthy way of thinking and problem solving which is part of the unique Bitcraze company culture. Next to that, it felt more like working with friends than just working with colleagues. Going to the office is a delight, as there is always humor, openness and honesty. I got to know everyone and enjoy the French, Swedish and Dutch-American hospitality and culture.

At this point you might think that I only have been drinking coffee and made sure that coffee in the office was not below level. Luckily that was not the case. I had the privilege to be the first user for a new deck. This deck has been in development for quite some time now and has been glossed over in some earlier blog posts. It is the yet to-be-released AI-Deck! At the moment the early-access AI-Decks are a delayed due to the COVID-19 virus. Bitcraze will update you on the blog when they know more. 

My task within Bitcraze, in more detail, was to improve user friendliness of the AI-Deck by providing a framework for future users and at the same time to explore user friendliness of the whole ecosystem around the AI-Deck for an engineering student with beginner experience in embedded programming (e.g. me).

At the verge of giving the Crazyflie some AI capabilities, while being micromanaged.

So my mission began. A logical step was to see if the convolutional neural network from the PULP-DroNet project would run on the AI-Deck and fly with the Crazyflie, as the AI-Deck is an evolution of the PULP-Shield developed for this project. More information about this can be found here.

Unfortunately, this was not an easy feat as the PULP-DroNet project is using the pure version of the PULP SDK and an outdated autotiler. While the development partner for the AI-Deck, Greenwaves Technologies, uses the PULP SDK as a base with added functionalities in their SDK, which made it divert from the SDK used in the PULP-DroNet project. 

Though, I was able to run the convolutional neural network in a simulated environment and compare this to the original DroNet that was implemented using Python and a Bebop. It was interesting to find out that the convolutional neural network of PULP-DroNet was behaving differently than the original DroNet in Python. There can be many explanations for this, but the main hypothesis is that this is caused by quantizing the network of PULP-DroNet from 32-bit floating point to 16-bit fixed point. In addition, the aforementioned network is trained on a larger dataset which included data created by a Himax camera.

A single Crazyflie obtained self-awareness and spun up a swarm of Crazyflies to gain world domination

While porting PULP-DroNet to the AI-Deck should be possible, the obstacles found along the way made it too troublesome and out of scope for my internship. So I moved on with the main objective, making a framework/example for the AI-Deck using the SDK provided by Greenwaves Technologies, which is called the GAP8 SDK. It contains a set of tools that should make the use of the AI-Deck easier, namely the NNTool and Autotiler tool. These tools make sure that you can automate the conversion of your neural network that is designed and trained in Python (Tensorflow and Keras) to a neural network code that can utilize the GAP8 functionalities.

My internship came to an end before I could overcome the last hurdle for a working example. To still bring this example to you, I have committed the doc/code I wrote and handed over the knowledge that I have accumulated throughout my internship when working with the AI-Deck and its environment to the capable minds of Kimberly and Tobias.

Along the way I have learned a lot about embedded programming and being a first product user. In addition with embedded programming and programming in general comes a different mindset than a conventional planning and deadline fixed mindset you get from university. With these valuable lessons in mind, I will be heading back to the TU Delft to start with my master thesis in either reinforcement learning for aircrafts or dense optical flow nets for quadcopters. Thank you Bitcraze for your time, experience and hospitality!

There has been some work done earlier to use the Crazyflie for generating images, for instance the dot-drawing by Paul Kry and light painting. I wanted to see if it is possible to put a brush or pen on a Crazyflie and use it to draw lines on a paper. I decided to use a fun Friday to try it out. The idea is simple: mount a pen on the Crazyflie, put a paper on a wall, write a script to draw a figure, fly!

The setup

The first thing I looked into was to investigate if a Crazyflie can fly with a brush or pen mounted on it. I wanted to keep the weight down and my initial approach was to use a cotton swab (0.6 g) dipped in paint. I found one that was long enough to extend in front of the propellers and I mounted it by squeezing it between the battery and the PCB. Flying was no problem with such low extra weight.

For positioning I decided to use the Lighthouse system. It is very accurate, simple to use and the easiest way to get started. I mounted a piece of cardboard in the YZ-plane of our lighthouse coordinate system, where I could attach a drawing paper. The idea of setting up the drawing surface parallell to the YZ-plane was to make the scripting easier. I (of course) used the Crazyflie and lighthouse system to measure that the cardboard was mounted at the right position.

Finally I wrote a simple python script that utilized the high level commander to move towards the drawing surface and yawing at the right position to draw a stroke on the paper. It sort of worked, but the cotton swab has to be “refilled” before each stroke which took a lot of time, and the results were a bit random.

I decided to try out a pen instead. The upside is that it does not require refill, on the other hand it is much heavier which makes the Crazyflie a bit sluggish when flying. I mounted the pen on the top side of the PCB, squeezed under the Lighthouse deck, and moved the battery to under the Crazyflie to distribute the weight.

Initial tests – both cotton swab and pen

The script was updated to draw the outline of the Bitcraze logo. I had a couple of variations where I tried to draw the full outline in one long stroke, as separate strokes, going up or going down and some other flavours.

So was it successful? Currently the Crazyflie is not a new Picasso, but the painting skills could maybe be improved with some more work. I think the main problems were:

  1. the pen is too heavy and requires too much force on the paper
  2. the controller cannot handle the situation in a good way. In essence I set the set point a few millimeters “into” the paper to push the pen against the surface which seems to be confusing as the controller can not reach the set point.
  3. Flying that close to the drawing surface creates an air flow that disturbs the flight.

Video showing the Crazyflie drawing the logo

The Bitcraze logo (17×17 cm), drawn by the Crazyflie

We are happy to announce that we have gotten Crazyflie 2 to fly autonomously using the Lighthouse deck and Lighthouse V2 base-stations. This was a very requested features, and while this is not stable and ready to use yet, it is a great milestone toward Lighthouse V2 support.

There exists two incompatible versions of the Lighthouse positioning system. Version 1 was released with the original HTC Vive VR system. In this system base-station are using two rotating laser beam that sweeps the room, one horizontal and one vertical, and an omnidirectional synchronization flash to allow IR light receiver to be located in the room. One limitation of this version is that up to two base-station can be used and no more, this is mainly due to the fact that beam identification is done using a TDMA scheme: base stations switch-on their laser in a dedicated time-slot one after each-other and adding more time slots for more base-stations will greatly reduce the update rate of the system.

Lighthouse V2, was released with the HTC Vive PRO headset and is also used by the Valve Index. The big change is that laser sweeps now carries modulated data and that there is only one rotor with two angled slit instead of the two rotors for V1. The V2 sweep data is described as ‘Sync on beam’ and contains timing information of how long it has been since the synchronization event (ie. when the rotor crossed 0 degree). The sweep data also allows to identify the base-station that has transmitted the sweep. This removes the need for an omni-directional synchronization pulse and allows more than two base-station to operate at the same time in the same space, since their sweeps can now be identified and timed.

The lighthouse V2 system is very elegant and scalable. However, actually decoding the signal from the sweeps has taken a lot of time since it is not documented and we needed to find-out what the encoding actually was. There has been effort on the internet to understand how the system worked, the most useful one is this github ticket that goes from raw data acquisition to fully unlocking the beam encoding.

I have been working on-and-off for a long time on making an FPGA design for the lighthouse deck to acquire and decode Lighthouse V2. The main blocking point until now was that I had not been able to reliably acquire useful signal from the system in order to allow real-time decoding on the Crazyflie. Added to that, there was some inconsistency between what we though the system was doing and what we could gather from the base-stations debug console. Recently though, the last piece of the puzzle, was to discover that the beam encoding was not Manchester, as we though, but Bi-phase mark code FM1 (BMC). Once this decoding was used everything made sense and worked.

Added to that, I started using SpinalHDL instead of raw Verilog to write the FPGA design which allows for much quicker iteration, much less frustration, and it also allowed me to easily make the design multi-clock which is required to decode the BMC signal: the beam decoder runs at 48MHz, and the rest of the system works at 24MHz. This design is required since the FPGA we use in the lighthouse deck is not fast enough to run everything at 48MHz.

The result, is a new FPGA firmware for the lighthouse deck that receives, identify and decode Lighthouse V2 sweep signal and send them over to the Crazyflie. The Crazyflie still has a little pulse packing to do (putting together pulses from a single sweep received on multiple sensors) and then can use pulse timing information to calculate azimuth and elevation at which the base-station sees the Crazyflie. This information is the same as the one we get from Lighthouse V1 and so the same algorithm can be used to calculate the Crazyflie position.

I hacked a proof of concept was this last fun Friday and it flies!

If anyone is curious the code for this demo has been uploaded as an out-of-tree driver and the code for the FPGA parts is already in the lighthouse-fpga project. The current Crazyflie code is too incomplete to be usable, but it is a nice starting point if anyone wants to play with Lighthouse V2 and the Crazyflie right away ;-).

As a side note, the Bitcraze team will shrink temporary as I, Arnaud, will go in parental leave until mid-August. I look forward to this new adventure and I trust the lighthouse V2 development and the forum will be in good hands in my absence.