@Myles-Levine , if you are masking out 90% of down-facing camera, then it is probably useless any movement of the features will get it out of the unmasked region very easily and feature will be dropped.. It may hurt VIO as there may be features going in and out, only trackable for a few frames (just adds to the complexity).

You should try to move the down-facing camera to free up it's FOV or even have it facing slightly angled to the back of the drone..

Alex

Hello @Catalystmachine ,

Sorry for the confusion. We have updated the page to be more clear that Boson is not included, see below: "(No Boson)" was added to the kit description.

The title of the product already states "VOXL 2 MIPI Boson+ Adapters for Thermal IR FPV" as well as the Kit description used to say (Before we added (No Boson)) : "PCB Adapters Only..."

The price of the Boson cameras varies significantly, depending on the specifications. You can contact a distributor to get more details. For example : https://www.oemcameras.com/product-category/thermal-imaging-cameras/thermal-imaging-cores/flir-boson-series-htm/boson-plus/ (you can find more at https://oem.flir.com/contact/find-a-dealer/)

Please note that Boson+ series of the Boson sensor is required for compatibility with VOXL2.

Regarding the pricing of the adapter kit, we do offer discounts in higher volumes, if you are interested, please send us a request: https://www.modalai.com/pages/contact-us

ebff1907-0495-4a65-8f27-2138562022d3-image.png

@qubotics-admin First you need to determine the version of PX4 running. With ssh or adb you can run the following at the command line: px4-ver all. You will see PX4 version and Vendor version. So, for example, for px4 version 1.14.0 and vendor version 8.134.3 then you would checkout the tag v1.14.0-8.134.3-dev from the repo to see that commit.

@SKA , I am not sure. please disable all streams except preview and try again. specifically disable:

small_video large_video snapshot

Double check to make sure:
en_raw_preview: true
en_preview: true
en_misp : true

It seems you have at least small_video enabled.

What camera resolution are you requesting? (preview_width, preview_height)

Alex

@cbay voxl-fpv-px4 is based on voxl-px4 but has some custom tuning code / parameters for our fpv drones that isn't in voxl-px4. So, it's kind of a superset. But there isn't anything related to low level i2c drivers so I have no idea how that could be causing this issue.

Hey @voxltester
It seems there is an issue with how MicroXRCE and the microhard modem interfaces are starting up, I had to edit the service files to get the topics on the VOXL2.
Edit /etc/systemd/system/voxl-microdds-agent.service:

[Unit] Description=voxl-microdds-agent SourcePath=/usr/bin/MicroXRCEAgent After=network.target After=multi-user.target [Service] User=root Type=simple ExecStart=/usr/bin/MicroXRCEAgent udp4 -p 8888 [Install] WantedBy=multi-user.target

Edit /etc/systemd/system/voxl-modem.service:

[Unit] Description=Modem Initialization After=sdsprpcd.service [Service] User=root Type=oneshot ExecStartPre=/bin/sleep 2 ExecStart=/usr/bin/voxl-modem-start RemainAfterExit=yes [Install] WantedBy=multi-user.target

Add export ROS_DOMAIN_ID=0 to your .bashrc
Reboot the VOXL2, and the topics should come up
Thanks

Hi @Alex-Kushleyev, sorry for the late reaction, it took some time before i could update to the new SDK (1.6). the system now works normally with the resolution 2704x1540, and downscaled to 1920x1080 for the MISP pipeline. Thanks for the help!

@ey , please send us a request via http://www.modalai.com/pages/contact-us

Alex

@Maxwell-Schaefer Thanks for your reply. Is this step also applicable for the BetaFPV Nano RX? Should this also used ModalAI's firmware repo? Or will this use BetaFPV's? I did look into trying to flash the Nano RX initially, but stopped short of doing it as the physical RX itself did not have a BetaFPV silkscreen on it, so I assumed that it might be using a different firmware from the stock BetaFPV

Are you able to advise on the other queries regarding the new wifi adapter board?

@Jetson-Nano , since we currently have no plans to do the MIPI integration of this sensor, you always have the option to use USB connection and voxl-uvc-server to publish images via MPA. You would need to check if the sensor supports the standard UVC interface and try it out...

Alex

@greg_s That would explain a lot what's happening.

If you start fresh and connect to the drone with voxl-portal and goto the VIO tab/menu option, you should be able to pick up the drone and perform a bench test.

Considering Figure 8 is not working, you will need to inspect the front and downward camera, tighten/remount any loose parts and possibly check camera focus & calibration.
Worse case if the forward camera is good and not changed from factory, you can edit /etc/modalai/vio_cam.conf on the drone and disabled the faulty camera (disabled the down camera).
You'll have degraded precision (mainly bad vio in fast yaw motions), but should be able to perform a figure 8 and should get you running until you repair that bottom sensor.

@bendraper
Hello,
I hope this quick clarification helps for warnings:

CMake Policy warnings: OV internally (external dir in the repo) still uses boost and references an older cmake version -- it's a harmless warning for devs (as you are building from scratch)

Yaml-cpp lib warnings: This is a third party dependency that gets compiled from scratch during the make/build process. We use C++ 17 but the library is based on an older C++ version; nevertheless, it compiles fine on C++ 17 with minor warnings. -- it's a harmless warning

TrackOCL warning: ModalFlow is a core library written in C++ 17 that allows function call usage as done in TrackOCL, i.e., the narrowing conversion is harmless in this case.

That being said, we are actively working on the dev branch, so all flight--impacting warnings are hopefully caught early. We currently have no flight-impacting warnings/bugs identified, but if you found one warning/bug that caught your attention, please feel free to share.

All the best,
ZBFT

@Vinny Perfect, thanks!

@Alex-Kushleyev Thank you so much!

I'm a bit preoccupied with a few other projects but hopefully I can take a look by the end of next week and get back to you.

Hey @svempati
I trained a model in the docker command as provided by you. I am able to run an inference on potholes following exactly the steps provided by @Zachary-Lowell-0 earlier.
Could I ask you what your voxl-tflite-version is? And if you havent yet, could you try to run this version: http://voxl-packages.modalai.com/dists/qrb5165/sdk-1.6/binary-arm64/voxl-tflite-server_0.5.1_arm64.deb
Make sure to change the .tflite model and labels as shown in the loom video after the install, then run voxl-configure-tflite

Thanks