@dlee ,

Yes the new TOF sensor (IRS2975C) is more powerful that the previous generation. What I mean by that is that it can emit more IR power but also heats up more. Emitting more power allows the sensor detect objects at larger distances or objects that are not as reflective.

In current operating mode, the auto exposure control is enabled inside the sensor itself, which modulates the emitted IR power based on the returns that the sensor is getting. That is to say, the power draw will vary depending on what is in the view of the sensor. If there are obstacles nearby, the output power should be low, otherwise it can be high. At full power, the module can consume close to 0.8-0.9W

So the first solution, if design allows, is to add a heat spreader to dissipate the heat, which you already started experimenting with. The sensor has a large exposed copper pad in the back for heat sinking purposes for this exact reason. Just be careful not to short this pad to anything, use non-conducting (but heat transfering) adhesive pad between the sensor and heat spreader.

In terms of a software solution to the issue, we can query the temperature of the emitter. We can also control the maximum emitted power used by the auto exposure algorithm. That is to say, still leave the auto exposure running in the sensor, but limit the maximum power that it is allowed to use.

We are planning to add some software protection that limits the maximum output power as a function of the emitter temperature. This will require some implementation and testing.

Meanwhile, please consider using a heat spreader, which will be the best solution if you want to make use of the full sensor's operating range and not have our software limit the output power in order to prevent overheating.

Hello @Kashish-Garg-0

we have a curve that is "motor voltage vs rpm", meaning that for a desired RPM, it tells the ESC what average motor voltage should be applied. The average motor voltage is defined as battery_voltage * motor_pmw_duty_cycle. The battery voltage in this curve is in millivolts. Since you are typically controlling the desired RPM, as a user you do not need to worry about what "throttle" or voltage to apply - the ESC does this automatically in order to achieve the desired RPM. this calibration curve is used as a feed-forward term in the RPM controller. The ESC does support an "open loop" type of control where you specify the power from 0 to 100%, which is similar to a standard ESC, but PX4 does not use that ESC control mode.

By the way, you can test the ESC directly (not using PX4) using our voxl-esc tools (https://gitlab.com/voxl-public/voxl-sdk/utilities/voxl-esc/-/tree/master/voxl-esc-tools) which works directly on VOXL2 or a standalone linux PC (or mac). voxl-esc-spin.py has a --power argument where you specify the power from 0 to 100, which translates directly to the average duty cycle applied to the motor.

Here is the calibration for the Starling V2 motor / propeller that we use:
https://gitlab.com/voxl-public/voxl-sdk/utilities/voxl-esc/-/blob/master/voxl-esc-params/mavic_mini_2/mavic_mini_2.xml?ref_type=heads#L63

Also, you can take a look at this post to see how to interpret those parameters a0, a1, a2 : https://forum.modalai.com/topic/2522/esc-calibration/2

We also have some dyno tests for this motor / propeller : https://gitlab.com/voxl-public/flight-core-px4/dyno_data/-/blob/master/data/mavic_mini2_timing_test/mavic_mini2_modal_esc_pusher_7.4V_timing0.csv . We are not sure how accurate that is, but it can be used as a starting point. @James-Strawson can you please confirm that is the correct dyno data for the Starling V2 motors?

Alex

@reber34 , perhaps this approach can work for you:

• record a video encoded at high bit rate (using voxl-camera-server and voxl-record-video . Please note that the output of voxl-record-video will not be in a standard container (such as mp4, etc), but you can fix it with ffpeg : ffmpeg -r 30 -i voxl-record-video.h264 -codec copy videofile.mp4
• re-encode the video offline with desired codecs / bit rates / resolutions
• install gst-rtsp-launch which uses gstreamer to set up an RTSP stream https://github.com/sfalexrog/gst-rtsp-launch/
  • you will first need to figure out what gstreamer pipeline to use on voxl2 that will load your video and parse the h264/h265 frames (can use null sink for testing) and then use that pipeline with gst-rtsp-launch which will take the encoded frames and serve them over rtsp stream.
• gstreamer may be more flexible for tuning the encoding parameters of h264/h265 (compared to voxl-camera-server) and you can also use it in real time later (using voxl-streamer, which uses gstreamer under the hood)

Another alternative is to use voxl-record-raw-image to save raw YUVs coming from voxl-camera-server and then use voxl-replay and voxl-streamer - the latter will accept YUVs from the MPA pipe and encode them using the bit rate that you want. Note that depending on the image resolution, YUV images will take a lot more space than encoded video, but maybe that is also OK since VOXL2 has lots of storage.

Alex

@smilon ,

I believe you are correct! Thank you. We will double check this and fix.

@Gary-Holmgren , you are correct, the high resolution camera is not used for VIO, we typically use it just for video recording / streaming.

You can certainly add a new transform to the extrinsics file and use it in your application. You should use the same name for the camera in the extrinsics file as you name it in voxl-camera-server.conf just to be consistent.

Hello @Gicu-Panaghiu,

I am going to assume you are using VOXL1, since you did not specify..

We do have RAW8 and RAW10 support for OV7251. The selection of the format has to be done in several places.

First, you have to select the correct camera driver, specifically..

ls /usr/lib/libmmcamera_ov7251*.so
/usr/lib/libmmcamera_ov7251.so
/usr/lib/libmmcamera_ov7251_8bit.so
/usr/lib/libmmcamera_ov7251_hflip_8bit.so
/usr/lib/libmmcamera_ov7251_rot180_8bit.so
/usr/lib/libmmcamera_ov7251_vflip_8bit.so

there are 5 options and one of them is _8bit.so which means it will natively ouptput 8bit data (all others output 10 bit data).

the driver name, such as ov7251_8bit has to be the sensor name <SensorName>ov7251_8bit</SensorName> in /system/etc/camera/camera_config.xml.

You can check camera_config.xml for what sensor library is used for your OV7251.

When you run voxl-configure-cameras script, it will actually copy one of the default camera_config.xml that are set up for a particular use case, and I believe it will indeed select the 8bit one - this was done to save cpu cycles needed to convert 10bit to 8bit, since majority of the time only 8bit pixels are used.

Now, you mentioned that HAL_PIXEL_FORMAT_RAW10 is passed to the stream config and unfortunately this does not have any effect on what the driver outputs. If the low level driver (e.g. libmmcamera_ov7251_8bit.so) is set up to output RAW8, it will output RAW8 if you request either HAL_PIXEL_FORMAT_RAW8 or HAL_PIXEL_FORMAT_RAW10.

So if you update the camera_config.xml to the 10bit driver and just keep the HAL_PIXEL_FORMAT_RAW10 in the stream config (then sync and reboot), you should be getting a 10 bit RAW image from the camera. But since the camera server is currently expecting 8 bit image, if you just interpret the image as 8 bit, it will appear garbled, so you will need to handle the 10 bit image (decide what you want to do with it) in the camera server.

@KnightHawk06 , use voxl-calibrare-camera tracking_down_misp_grey <remaining options>

The ipk is available here now : http://voxl-packages.modalai.com/stable/voxl-hal3-tof-cam-ros_0.0.5.ipk - you should be able to use the launch file to choose between two modes (5=short range and 9=long range) and fps, which are listed in the launch file.

@Prabhav-Gupta , yes it seems like OpenCV and ROS YUV_NV12 formats do not match up. I will take a look at it.. it seems the ROS YUV is packed (interleaved) while standard for storing YUV NV12 is having two planes : plane 1 : Y (size: widthheight), plane 2 : UV (size: widthheight/2)

In the meantime.. you can stream a rtsp h264/h265 from VOXL (use decent quality so that image looks good) and use opencv to receive the stream and get decompressed images: https://stackoverflow.com/questions/40875846/capturing-rtsp-camera-using-opencv-python

Would that work for you ? (unfortunately with rtsp stream, you will not get the full image metadata, like exposure, gain, timestamp, etc).

RTSP streaming can be done using voxl-streamer, which can accept either a YUV (and encode it) or already encoded h264/5 stream from voxl-camera-server.

Alex

@cfirth , if you are streaming raw RGB images, then 6404803 is 921KB, at 30fps will be 27MB/s which is definitely more than you want to transfer over wifi for a single stream. You can measure the available bandwidth using iperf as i suggested in previous post. Consider using compressed image format in ROS or use h264/5 encoded streams. You can use voxl-streamer to encode the tracking frames into a streaming video. If you describe your use case in more detail, we can provide additional suggestions.

@mkriesel , I have updated the boson configuration and read scripts (boson_read.py, boson_set_8bit.py, etc) to include configuring additional settings like mipi start state, dvo output interface, etc. Please download them again (from links earlier in this thread) and try again. Also, just to confirm that the original Boson setup instruction using the FLIR GUI should work for 8 bit format.

Just one note, even though I set myCam.dvoSetOutputFormat(FLR_DVO_OUTPUT_FORMAT_E.FLR_DVO_IR16), where FLR_DVO_IR16 = 3, when i read back the configuration, i get the default format (2) : FLR_DVO_OUTPUT_FORMAT_E.FLR_DVO_DEFAULT_FORMAT = 2 . It seems that in your output the ouput format is set to 3 - i am not sure why, there could be some minor quirks in the boson firmware.

In any case, please try the GUI and updated scripts and let me know how it goes!

Alex

@mkriesel

Please make sure that you only have one sensormodule file for boson for the given configuration in /usr/lib/camera/.

One thing to check is to see if the Boson is in active mipi state by default. You can add this to your boson_read.py file :

print('dvoGetMipiStartState: ')
result, mipi_start_state = myCam.dvoGetMipiStartState()
print(result)
print(mipi_start_state)

print('dvoGetMipiState: ')
result, mipi_state = myCam.dvoGetMipiState()
print(result)
print(mipi_state)

My output is:

dvoGetMipiStartState: 
FLR_RESULT.R_SUCCESS
FLR_DVO_MIPI_STATE_E.FLR_DVO_MIPI_STATE_ACTIVE
dvoGetMipiState: 
FLR_RESULT.R_SUCCESS
FLR_DVO_MIPI_STATE_E.FLR_DVO_MIPI_STATE_ACTIVE

FLR_DVO_MIPI_STATE_ACTIVE is 2, FLR_DVO_MIPI_STATE_OFF is 0

If your sensor does not show active start state, then it may need to be set using the following:

myCam.dvoSetMipiStartState(FLR_DVO_MIPI_STATE_E.FLR_DVO_MIPI_STATE_ACTIVE)

If this still does not work, it is possible that my script does not do the full setup for mipi output, so you may want to try using the FLIR GUI for the initial setup (for 8 bit post AGC and use the default 8 bit sensormodule): https://docs.modalai.com/M0153/#boson-software-setup

Alex

@jameskuesel ,

I believe you need to update your voxl-mongoose library to latest, from here : http://voxl-packages.modalai.com/dists/qrb5165/dev/binary-arm64/ . I believe voxl-portal recently switched to using updated version of that package and that is what is being pulled in when you build voxl-portal from dev or EIS branch.

Alex

@akirahrkw ,

Even if the checkerboard is detected, it needs to fill the majority of the non-shaded region. In order to progress to the next step, you need to bring the camera / pattern closer together and then the procedure will accept it. I know this is not obvious when you do it for the first time.

Regarding voxl-camera-server freeze, what if you run voxl-inspect-cam <camera_stream> on voxl2, does it still show images coming in?

Try setting your voxl into perf mode (voxl-set-cpu-mode perf) which will speed up the calibration pattern detection. I see that the calibration is only running at 2.8FPS at 1280x800 resolution.

During calibraiton, you may also want to stop other services that might be doing image processing, so that VOLX2 has more cpu cycles for the calibration app.

Alex

Hi @Riccardo-Benedetti and @paul-foley ,

This issue is not common. There is a potential condition (which we are going to look into) when a MPA client crashes and does not close the pipes properly, the MPA server side will keep the allocated resources open. If the client crashes repeatedly, eventually there will be no more memory for the Kernel to allocate for the new pipes.

Please check to make sure that you have no client processes that are subscribing to image streams and are crashing and restarting (perhaps restarting automatically by systemd). It is possible that a process that is not your test app is misbehaving for some reason and is causing this memory leak by continuous crashing and re-starting.

Also, what if you just use voxl-inspect-cam to inspect the single camera stream and no other camera clients running, does the same issue happen?

Alex

@mkriesel , oh yeah, i now that dpkg stuck issue..

use dpkg --force-all -i <package.deb> , you can install voxl-cpu-monitor and libmodal-pipe together or separately.

Alex

voxl-portal, as demonstrated in the videos linked above, has been updated in the eis-integration branch (merged latest from dev branch, so it can be built with the latest voxl-cross:4:4 now).

https://gitlab.com/voxl-public/voxl-sdk/services/voxl-portal/-/tree/eis-integration

@mkriesel ,

I did a basic test of configuring Boson for external sync using bosonSetExtSyncMode(FLR_BOSON_EXT_SYNC_MODE_E.FLR_BOSON_EXT_SYNC_SLAVE_MODE) in the FLIR python API.

If the sync signal is absent, then Boson just stops sending frames (as expected). Once I installed the missing resistor on the ModalAI Boson adapter to patch the common 30FPS camera sync signal from VOXL2 to Boson, I started receiving frames from Boson at 30FPS. Adjusting the sync signal from 30 to 60FPS resulted in some FPS instability (as well as some minor flickering artifacts) in Boson frames due to the fact that the sync signal coming out of voxl2 has some jitter. Boson timing at 60FPS is very tight, so sync signal coming in too early can cut off the previous frame, so i believe that was happening.

I also noticed that when the sync signal was at 30FPS, the FFC would take twice as long, which is interesting. I wonder if Boson indeed expects 60FPS sync signal and perhaps something may not work properly otherwise. will need to check Boson documentation..

Also keep in mind that the tracking cameras (AR0144) cannot operate at 60FPS in sync'ed mode (different story). So, if you are using sync'ed AR0144 tracking cameras and would like to use 60FPS sync'ed Boson, this may not be possible with the same sync line.

I will need to experiment with this some more. Can you please elaborate on the use of Boson sync in your application (and whether you need VOXL2 to generate the sync signal or you have your own sync signal)?

Alex

@jameskuesel ,

The documentation correctly lists the input resolution as 4040x3040. Even though the camera's maximum resolution is 4056x3040, we can set up the camera driver to request any smaller resolution from the camera. The reason for using width=4040 is that the hardware-optimized debayering functions on the GPU require a specific row alignment (in bytes) and the width of 4040 results in the line stride that is compatible with the gpu functions. If width of 4056 is selected, the camera does send a valid RAW10 bayer image, but the gpu cannot correctly de-bayer it.

You can view all the raw supported resolutions by running voxl-camera-server -l and take a look at the following (which i am pasting for the IMX412 driver that you should be using). You will see duplicate resolutions because there are different FPS supported at the same resolution (the FPS are not printed).

ANDROID_SCALER_AVAILABLE_RAW_SIZES:
These are likely supported by the sensor
4056 x 3040
4040 x 3040
4040 x 3040
3840 x 2160
3840 x 2160
3840 x 2160
1996 x 1520
1996 x 1520
1996 x 1520
1936 x 1080
1936 x 1080
1936 x 1080
1936 x 1080
1936 x 1080
1996 x  480
1996 x  480
1996 x  240
1996 x  240

The details about all the camera modes supported by the imx412 EIS driver can be found here : https://docs.modalai.com/camera-video/low-latency-video-streaming/#imx412-operating-modes

Please follow the instructions step by step and EIS should work.

EIS zoom and drag is supported. You can test zoom easily by using voxl-portal, click a small check box in bottom left corner to enable advanced panel. You can use mouse wheel or zoom slider to control the zoom.

See demo videos here :
ROI features: https://www.youtube.com/watch?v=FXv4855WjNc
EIS: https://www.youtube.com/watch?v=fi2BO_U5f-c

(and a screenshot):

Zoom will work with the default version of voxl-portal, but in order to use the drag feature, you need to use voxl-portal from eis-integration branch : https://gitlab.com/voxl-public/voxl-sdk/services/voxl-portal/-/tree/eis-integration . Actually it looks like it has not been updated in a while, let me check if it still builds fine and i can share a deb.

Under the hood, the voxl-portal GUI sends commands to voxl-portal back-end running on VOXL2, and that, in turn, sends commands to the camera server to change the zoom and drag. For zoom, indeed a command set_misp_zoom is used, such as:

voxl-send-command hires_misp_color set_misp_zoom <zoom> <optional: convergence_delay>

zoom should be 1.0 or greater, convergence delay 0.0-1.0 -- (0.0 instant, 1.0 : infinitely long)

By the way zoom also works with MISP when EIS is disabled.

Let me check to make sure the voxl-portal from the EIS branch works and I will also document the zoom / drag command syntax.

Alex

@mkriesel , just to confirm, does this test work (run) on other voxl2 boards for you?