@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

@jimbow77 and @oasisartisan ,

TOF V1 (which is now EOL, so you don't have it on Starling 2 Max) was set up the following way:

• The calibration data was downloaded the first time the sensor was used and stored in /data/misc/camera/, then re-used for next time
• calibration files include pmd.spc, tof_cal_eeprom.bin and irs10x0c_lens.cal - the latter likely containing the intrinsics calibraiton data (although i have not checked explicitly)

TOF V2 (what is shipping in Starling 2 (and Max) ), is set up so that the TOF Library downloads (via i2c) the calibration data each time the voxl-camera-server starts. This is done internally to the TOF libraries. I don't know exactly why there is a difference between how the calib data is handled for two sensors by the TOF software, but this is how our sensor vendor helped us set it up. The TOF processing software downloads the calibration from the sensor each time voxl-camera-server starts and applies it to generate the point cloud. It may be possible to get the TOF intrinsics using the TOF library API, but we have not checked.

Do you have a specific reason why the TOF intrinsics are needed?

If you wanted to calibrate the TOF sensor yourself, then you would either need a checkerboard some material that is IR reflective ("white") and non-reflective ("black"). Alternatively, you could use an array of IR leds (or IR reflective dots + IR light) and use dot pattern detector as opposed for checkerboard for calibration.

Regarding Hi-res cameras, the intrinsics are not calibrated and not included. It can be calibrated using our camera calibration app (voxl-camera-calibration), but you should do it at half resolution or smaller (not full size like 4056x3040), otherwise the app runs very slowly. If you need more details how to calibrate at high resolution, let me know (involves downscaling exactly by a factor of 2 and then upscaling the resulting intrinsics)

Alex

@smilon , voxl-esc-calibrate.py is a script that runs a test procedure in a single motor (with propeller mounted) to calibrate the behavior of the motor / propeller. This procedure only needs to be run once if you change motor or propeller type from a default configuration. The output of this script is just 3 coeficients a1, a2, a3 which you would need to manually enter into an ESC calibration xml file and then upload the xml paramer file to the ESC. Full details about the ESC calibration (when to do it and how) can be found here : https://gitlab.com/voxl-public/voxl-sdk/utilities/voxl-esc/-/blob/master/voxl-esc-tools/calibration.md?ref_type=heads

If you are using standard motors and propellers (one of standard ModalAI drones), you do not need to run this calibration procedure.

It sounds like you got it working, I believe voxl-configre-mpa took care of it. You can see what voxl-configure-mpa typically does here : https://docs.modalai.com/voxl-configure-mpa/ , which includes running voxl-esc to upload the latest firmware and params for a specific vehicle.

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.

@jakkkkobo ,

The source code of voxl-send-neopixel-cmd test tool is here : https://gitlab.com/voxl-public/voxl-sdk/services/voxl-io-server/-/blob/master/tools/voxl-send-neopixel-cmd.c

The function that creates a binary packet (which is forwarded to the ESC via PX4) is here : https://gitlab.com/voxl-public/voxl-sdk/services/voxl-io-server/-/blob/master/lib/modal_io.c#L181 . Basically you need to provide the following information to this function:

• number of LEDs you are controlling
• LED type (RGB or RGBW)
• array of LED colors (3 bytes for each LED in case of RGB, or 4 bytes per LED for RGBW).
• the array should contain all LED values ranging from 0-255, so if you had 3 LEDs and first should be red, second green and third blue (50% power), the LED color array would be : [127, 0, 0, 0, 127, 0, 0,0,127].
• this function will create a packet with checksum and send it to PX4, and PX4 will forward the packet to the ESC.

You can modify the voxl-send-neopixel-cmd to do what you need, maybe you can make it accept an array of LED color values via command line.

I know the tool could have been more helpful. If you describe your use case, maybe I can help improve the voxl-send-neopixel-cmd tool.

Alex

@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.

@Moderator said in VOXL ESC Mini 4-in-1 Current per Motor:

Is it possible to step up voltage?

Can you please clarify the question?

Mini ESC is designed for small drones ( < 500g ). The ESC has been tested to handle 15A continous at 15V input continuously (60+ seconds), but with full direct air flow from propellers. This would simulate a full throttle "punch-out" on a small FPV drone (high current, but also lots of direct airflow = cooling). Do not use this ESC if the drone needs 10-15A per channel just to hover. Use it in application where hover current per motor is less than 5A (ideally 2-3A which is very typical) and absolute maximum continuous current per motor can be 10-15A.

For example, motors used for small FPV drones often are around 1306 size (3-4S Lipo). Those motors are usually rated for up to 10-12A continous (for 30-60 seconds). Larger motors can be used as long as maximum motor current does not exceed 10-15A (still 2-3A at hover) and there is sufficient cooling.

Always check ESC board temperature during initial flights / tuning. Temperature must stay below 110C at all times (critical), typically in the range of 40-70C for most applications. The ESC will most likely fail above 125C.

Temperature of the ESC board is the limiting factor because the board is so small. Mosfets can handle a lot of current as long as they don't overheat. So the design of the drone is very important (either use low current so that temperature is not an issue or properly design air flow from propellers and/or add heat spreader to keep the ESC board temperature in normal range for higher current draw applications).

ESC provides real time temperature feedback and it can be viewed in PX4 / QGC. Additionally, the PX4 logs contain the temperature information.

@Igor , we just uploaded the emulator docker image, please see details in this post : https://forum.modalai.com/topic/5179/qrb5165-emulator-image-1-6

@huy ,

The latest qrb5165 emulator docker image is v 1.5. We have been updating our downloads section, so it was inadvertently removed.

Right now, this docker image is available in the Downloads / Miscellaneous section and is called 865-rootfs_m0054_qrb5165-emulator-v1.5.tar. You can download it from there.

However, the name and location of this docker image is slightly incorrect. It will be renamed to qrb5165-emulator-v1.5.tar and moved to the Docker Images section soon. So if you cant find it under Miscellaneous section, please look at the Docker Images section.

https://developer.modalai.com/

Alex

Hi @Myles-Levine ,

I just want to double check something to make sure there is no confusion.

• The lepton sensor is an IR (thermal) camera. it does not measure range. The lepton sensor plugs into the large square socket that is mounted to the M0157 / M0187 board (they are very similar).
• The infrared TOF sensor is a smaller (rectangular) sensor, which is marked as U1 on M0187 : https://docs.modalai.com/M0187/ .

Have you checked whether the Lepton sensor is working using voxl-portal, which would allow you to look at the IR image coming from the sensor?

Alex

@Myles-Levine , sorry I thought you were referring to the other TOF sensor.

You will need a new M0187 board : https://docs.modalai.com/M0187/ . Please reach out to us via this form https://www.modalai.com/pages/contact-us , since we don't have that part available on the official page.

Is your Lepton sensor working properly?

Alex

@Myles-Levine , you can purchase the TOF sensor here : https://www.modalai.com/products/m0178?variant=48528287793456

@yaoyuh , it looks like the order has been taken care of. please let us know if you have any more questions.

Alex

@bendraper , have you considered running a docker container (VM) on your Windows machine with a minimal Linux kernel that would use the Linux drivers for NCM and create the proper network connection between VOXL2 and Linux container. Then you could potentially set up the routing table in Windows so that it can access the device via the Linux container and linux container may need to have some networking magic down as well to pass through the communication?

Alex

@Igor , sorry, we are working on restoring it.

@Gerhold-Ten-Voorde , have you considered adding a spacer between the camera sensor PCB and the current lens holder? At least for testing purposes, that should work.

I will check if we can share the lens holder part numbers.

Alex

OK, some updates..

• the reason why the resolution 9216x6944 was not accepted for snapshot is that the camera pipeline has a list of allowed resolutions and that resolution was not one of them. I could add the resolution 9216x6944 to the list, but actually another resolution (9216x6912) was already supported, so i updated the ov64b driver to include that. The latest driver is available here : https://storage.googleapis.com/modalai_public/temp/ov64b/20260417/ov64b_20260417.zip

  • this driver has the following resolutions : 9248x6944, 9216x6944 9216x6912 and the other lower resolutions. You can use 9216x6912 since it's compatible with misp and isp snapshot.

• I also added 9216x6912 resolution to the supported misp resolutions in camera server (dev branch) as well as allowed using misp with snapshot (previously it would throw an error since we thought that previously they were not compatible).

• Actually, snapshot does work with raw_preview + misp, but here is a warning that gets printed continuously:

WARNING: preview buffer pool for Cam(hires), Frame(30) has 0 free, skipping request

we can work around this warning by adding the following line to the voxl-camera-server.conf for the ov64b camera. this limits the number of buffers that are queued up for the streams and resolves the warning.

"max_request_queue_depth": 6,

So with these changes, you should be able to use two configurations

• preview + raw_preview + misp + snapshot
• small_video + snapshot

Please use the latest camera server from dev branch.

Right now, if you enable misp + small+video + snapshot, it does not work properly, but i will try it figure it out. Here is an example set up for small video + snapshot, but you can easily change it to use misp by disabling small_video and enabling preview + misp (and set auto exposure to "auto" from "isp" (auto will use misp auto exposure)).

Please try it out. I do agree that the ISP output has better overall image quality than MISP, also related to pretty significant lens shading of this small lens on ov64b. We will work on improvements of misp image processing.

For now, you just need to decide whether you need both RAW bayer and ISP JPG, in which case you cannot use small_video for streaming (until i resolve the issue). This means slightly worse image quality for live streaming. But if you are ok with ISP jpg, you can disable misp and use small_video stream for streaming.

By the way, i did not see any artifacts in the jpeg from ISP. Can you please let me know if the artifact is in every frame and where exactly it is located? or is the occurrence and location random?

{
                        "type":                 "ov64b",
                        "name":                 "hires",
                        "enabled":              true,
                        "camera_id":            1,
                        "fps":                  30,
                        "en_preview":           false,
                        "en_misp":              false,
                        "max_request_queue_depth": 6,
                        "preview_width":        9216,
                        "preview_height":       6912,
                        "en_raw_preview":       true,
                        "en_small_video":       true,
                        "en_large_video":       false,
                        "en_snapshot":          true,
                        "ae_mode":              "isp",
                        "gain_min":             100,
                        "gain_max":             1600,
                        "misp_width":           1600,
                        "misp_height":          1200,
                        "misp_venc_enable":     false,
                        "misp_venc_mode":       "h265",
                        "misp_venc_br_ctrl":    "cqp",
                        "misp_venc_Qfixed":     38,
                        "misp_venc_Qmin":       15,
                        "misp_venc_Qmax":       50,
                        "misp_venc_nPframes":   29,
                        "misp_venc_mbps":       30,
                        "misp_venc_osd":        false,
                        "misp_awb":             "auto",
                        "misp_gamma":           1,
                        "misp_zoom":            1,
                        "ae_desired_msv":       110,
                        "exposure_min_us":      20,
                        "exposure_max_us":      33000,
                        "exposure_soft_min_us": 5000,
                        "ae_filter_alpha":      0.6,
                        "ae_ignore_fraction":   0.2,
                        "ae_slope":     0.05,
                        "ae_exposure_period":   1,
                        "ae_gain_period":       1,
                        "small_video_width":    1600,
                        "small_video_height":   1200,
                        "small_venc_mode":      "h264",
                        "small_venc_br_ctrl":   "cqp",
                        "small_venc_Qfixed":    30,
                        "small_venc_Qmin":      15,
                        "small_venc_Qmax":      40,
                        "small_venc_nPframes":  9,
                        "small_venc_mbps":      2,
                        "en_snapshot_width":    9216,
                        "en_snapshot_height":   6912,
                        "exif_focal_length":    3.1,
                        "exif_focal_length_in_35mm_format":     17,
                        "exif_fnumber": 1.24,
                        "snapshot_jpeg_quality":        75
                }