Best posts made by Alex Kushleyev

@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.

@tonygurney , I am assuming that you are using the ESC hardware version M0134 (you can confirm using voxl-esc scan tool.

Here is the datasheet for M0134.
https://docs.modalai.com/modal-esc-datasheet/

This ESC has an option (via installing a 0 ohm resistor) to provide 3.3V to pin 1 of J3. This is the same voltage that is used for the MCUs that run on the ESC and should be used with caution (if this voltage rail is brought down by overloading, the ESC will shut down). Please see image below that shows where 0 ohm resistor should be (carefully) installed to enable 3.3V on pin1 of J3 (marked in red)

If you actually wanted 5V (VAUX), that is available on the other side of the board via two test points at the edge of the board. This voltage rail is provided by a separate switching regulator. See the following section : https://docs.modalai.com/modal-esc-datasheet/#neopixel-led-support

If you REALLY need 5V to be present on pin1 of J3, then you can add a small wire jumper from the VAUX test point directly to the pin1 of J3 (which is marked with a white dot). This pin 1 of J3 is normally disconnected. Please only do this if you are comfortable performing this type of soldering and we cannot guarantee the results :).

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.

@david-moro

If you look at this image, https://docs.modalai.com/M0169/#image-of-working-module it shows the TOF sensor (right) and the M0169 adapter (left).

The weight of the TOF module is 0.72g and weight of M0169 adapter is 0.93g.

This particular combo requires an external power input (4-5V as described here https://docs.modalai.com/M0169/#current-consumption). "External" in this case means additional power source besides the camera connector. The additional power input connects into M0169 via 2-pin JST connector.

The worse case power consumption is ~0.9W, which is a lot for such small module. The current draw plot shows a typical 5hz operation at maximum "exposure", which sends out 8 6-ms pulses of 1.0-1.1A (at 3.3V). so that is 1.1A * 3.3V * (5hz * 8 * 0.006s) = 0.87W. If you select 15Hz, the maximum exposure (emitter pulse time) will go down from 6ms to 2ms, so the maximum total power will remain the same.

This module would definitely require a heat spreader to operate continuously at this power. However maximum power output (even in auto exposure mode) can be limited to accommodate specific power dissipation capabilities. Additionally, the temperature of the emitter is reported from the TOF sensor.

Due to additional complexities that come with this new module (need for external power, which cannot be obtained from the camera connector) and heat management, we are working on the solution that would provide a positive user experience, so hence the delay.

I will check on the ETA

Hi

If you are looking for more low level functionality and a bit easier to experiment with, you may want to check out https://gitlab.com/voxl-public/utilities/voxl-rtsp/-/tree/dev . This tool is good for testing or running simple scenarios. This tool accepts some commands (like exposure control) via command line, so you could add a simple feature to save an image upon a command line input. Please see README to see how the exposure control input works and code for how it's implemented.

Please make sure that voxl-camera-server is disabled when you run voxl-rtsp.

If you still would like to go through the voxl-camera-server approach, we may need some more input from respective devs :).

I hope this helps..

Alex

It looks like your machine does not have QEMU support or is missing ARM support on your host machine. Please try instructions here : https://www.stereolabs.com/docs/docker/building-arm-container-on-x86/ (which also show you how to run ARM64 docker images on x86)

Hi Ryan,

Does the micrortps agent need to open a standard UART port, such as /dev/ttySx or /dev/ttyUSBx or similar? how hard is it to extend the micrortps client to use libvoxl_io functionality to access the serial port via libvoxl_io instead of standard serial port libraries? You would need to use this header https://gitlab.com/voxl-public/core-libs/libvoxl_io/-/blob/master/lib/include/voxl_io.h and link against libvoxl_io.

If changing micrortps code is not an option, then some sort of bridge could work. I have done similar things before using pseudo terminals (pty/tty pairs). Basically the bridge could be the master and would read/write using ptyXY and micrortps would be the client and read/write using ttyXY. However, i don't think pseudo terminals are available on VOXL.

https://stackoverflow.com/questions/52187/virtual-serial-port-for-linux this discusses the pseudo terminal as well another solution using socat. Now, socat is not available on VOXL either, but if you use docker, it can be installed in a small container using apt-get install socat.

Let me check if we can easily add the socat and pseudo terminals to the VOXL system image..

It seems perhaps the most robust solution (for now) would be extending micrortps to work with libvolx_io serial ports..

Alex

@tonygurney , yes.

Please see the comment about pin availability and pin numbers here : https://gitlab.com/voxl-public/voxl-sdk/utilities/voxl-esc/-/blob/master/voxl-esc-tools/voxl-esc-pwm.py?ref_type=heads#L17

Please note that running voxl-esc-pwm.py script will use the serial port and cannot be done concurrently with PX4. This is a standalone tool, unlike voxl-send-esc-pwm-cmd tool, which sends a message to voxl-px4 and the pwm control message is then forwarded to the ESC by the voxl_esc px4 driver.

Alex

@Aaky ,

You have good questions. Let me try to answer

1. We are working on some helper tools for our customer use to aid with evaluating the quality of focus. The tool will display an augmented image in voxl-portal, showing the results of the analysis (how sharp the image is). In general, you want to take a high contrast object (checkerboard), place it in the area where you want to focus and adjust the focus until the image is sharpest. The depth of field of a fisheye camera should be very large (everything beyond a small distance (a few inches) should be in focus). This means that placing an object 1-2 feet away from the camera can be easily used to calibrate the focus. I hope to share an initial version of the tool by the end of this week. Meanwhile, you may try to focus the camera by eye while having the camera pointed at a high contrast object

2. This "shadow" artifact happens for two reasons : fisheye lens blocks more light at extreme angles and also camera sensor CRA (chief ray angle) contributes to reduced sensitivity at large angles. The standard way to fix this in camera pipeline is using a lens-specific tuning file, which contains a Lens Shading Correction table and the ISP can do it without any CPU overhead. However, for tracking cameras, we use RAW images (not going through ISP image processing) to avoid any image processing artifacts, so this lens shading correction feature is not available. Nevertheless, the image can be corrected on the CPU and it should not be too expensive to do that. The best place to do that would be during the 10->8 bit conversion that happens on the CPU (camera sends 10 bit data and we convert to 8 bit to use for feature tracking). Using 10 bit data will provide better quality image and will save cpu cycles because the pixels will already be in memory. The 10->8 bit conversion is happening in the voxl-camera-server application. This feature is something we discussed internally, but we have not implemented it yet. the corners of a fisheye camera are usually of a lesser value because the features there are going to be stretched, lower quality (due to optics) and may not appear in the frame for a long time. Nevertheless, with corrected image brightness in the corners, the performance of VIO could be even better.

3. The exposure control algorithm that we implemented attempts to achieve a certain mean pixel (sample) value (MSV). it takes average of all pixels and if it is below the desired one, it increases gain and exposure to achieve the higher MSV. Gain and exposure both contribute to increasing image brightness, but they have their own side effects: long exposure will cause blur during motion and high gain will increase pixel noise. the QVIO algorithm applies a gaussian blur before doing feature detection / tracking, so it would be preferred to use higher gain than higher exposure and this is what we tuned for ov7251. I think going with those settings is a good start. However, the previous issue of dark corners can throw off the exposure control, driving image in the center to be too bright. The image you provided seems a bit too dark to my eye, perhaps you can increase the target MSV and see if that helps? were you able to track good features with QVIO using AR0144?

4. We are slowly switching over to using AR0144 cameras for tracking (VIO). In general M0149 camera module is a better camera module all around compared to ov7251 - better and higher resolution image sensor, better lens, so we expect better QVIO performance. I will double check with the team about any specific tuning for better QVIO performance using AR0144.

.. or if you wanted to continue using voxl-camera-server for 4K, just modify the server to save yuv frames on-demand and convert to jpeg later, as we discussed above..

@TomP , oh yeah, my bad.. you found the right one

I am not sure how to resolve the authentication issue, but you can always use docker save myimage:latest | gzip > myimage_latest.tar.gz to a tar file, then docker load on another machine and push to docker hub.

Also, by the way, you can build your docker image for VOXL on a regular linux machine. Qemu allows you to emulate arm and arm64 architectures and Docker supports this. This is how the voxl-emulator runs on x86-64: So, basically you can build your docker image on a x86-64 host and then push it to docker hub or copy directly to VOXL.

I know that does not answer your question, but hopefully there is a workaround here..

Alex

@austin-c , i don't have any concerns for disabling active freewheeling other than the fact that the responsiveness of the ESC will be reduced, as the RPM reduction will be purely due to air drag acting on the propeller (and small friction in the motor).

I can set up the MN4006 motor with a 13 or 15 inch propeller, do a RPM control tune and compare the RPM response results with regen on and off. This is actually pretty quick to do, i will try to do it in the next few days.

Please ping me if you don't hear back by early next week

Alex