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The amount of compute that sensor fusion uses is miniscule compared to running a NN or computing stereo depth maps. Sensor fusion runs in the background of your

The amount of compute that sensor fusion uses is miniscule compared to running a NN or computing stereo depth maps. Sensor fusion runs in the background of your phone the whole time to power things like [0] for example. 0. https://sensor-js.xyz/demo.html https://sensor-js.xyz/demo.html

Nomentatus 4y ago
Well, yes and no. Integrating the data and adjudicating conflicts between sensors is a real task, too. Also having just two opinions doesn't necessarily help if they conflict, and the lidar is the thinnest source of data. How do you coin flip that? You likely end up just discarding the Lidar's conflicting opinion.
- snovv_crash 4y ago
  LIDAR data might be more sparse but it is also more reliable, being an active sensor so it isn't affected by night. The real way is to use the LIDAR to add to the depth probability distribution in the stereo depth estimation. This way you aren't throwing any data away. LIDAR often gives probabilities as well, for example, and this can be used to eliminate reflections.
01100011 4y ago
You are confused my friend. Reading sensor data is not the same as feeding that data to a neural network and asking it to form a worldview composed of possibly conflicting sensor data streams(i.e. lidar vs vision vs ultrasonic). You are somewhat correct that it is quite trivial to read sensor data. For many sensors, there is some work which needs to be done to denoise or cleanup the input data. That's not where the story ends, however.
- snovv_crash 4y ago
  In order to display the gravity-aligned acceleration, sensor fusion between gyro and accelerometer has to occur. This is typically done with a Kalman filter, and runs on 1960s levels of hardware. If you look at something like a drone autopilot, eg. Ardupilot, the sensor fusion is soo cheap that they even extended the Kalman filter to also estimate things like the sensor bias offsets and earth's magnetic field vectors. Sensor fusion is computationally cheap. It's just a lot of R&D to do it in a way that leads to a net gain in precision and robustness.
  - 01100011 4y ago
    We're talking about radar and ultrasonic sensors here, not accelerometers. We're also talking about feeding them to a deep neural network. Not the same thing. Sensor fusion is not being done with a Kalman filter in this case.
    - snovv_crash 4y ago
      Radar and ultrasound both give drastically less data than a simple 720p webcam. After postprocessing their output bandwidth is more similar to a 9-axis IMU than a camera.
      - 01100011 4y ago
        Yes and each time you process the incoming camera data in the neural network you have extra calculations due to the sensor fusion with another data source, regardless of its bitrate. Have you worked with deep learning much?

Nomentatus 4y ago

Well, yes and no. Integrating the data and adjudicating conflicts between sensors is a real task, too. Also having just two opinions doesn't necessarily help if they conflict, and the lidar is the thinnest source of data. How do you coin flip that? You likely end up just discarding the Lidar's conflicting opinion.

snovv_crash 4y ago
LIDAR data might be more sparse but it is also more reliable, being an active sensor so it isn't affected by night. The real way is to use the LIDAR to add to the depth probability distribution in the stereo depth estimation. This way you aren't throwing any data away. LIDAR often gives probabilities as well, for example, and this can be used to eliminate reflections.

01100011 4y ago

You are confused my friend. Reading sensor data is not the same as feeding that data to a neural network and asking it to form a worldview composed of possibly conflicting sensor data streams(i.e. lidar vs vision vs ultrasonic). You are somewhat correct that it is quite trivial to read sensor data. For many sensors, there is some work which needs to be done to denoise or cleanup the input data. That's not where the story ends, however.

snovv_crash 4y ago
In order to display the gravity-aligned acceleration, sensor fusion between gyro and accelerometer has to occur. This is typically done with a Kalman filter, and runs on 1960s levels of hardware. If you look at something like a drone autopilot, eg. Ardupilot, the sensor fusion is soo cheap that they even extended the Kalman filter to also estimate things like the sensor bias offsets and earth's magnetic field vectors. Sensor fusion is computationally cheap. It's just a lot of R&D to do it in a way that leads to a net gain in precision and robustness.
- 01100011 4y ago
  We're talking about radar and ultrasonic sensors here, not accelerometers. We're also talking about feeding them to a deep neural network. Not the same thing. Sensor fusion is not being done with a Kalman filter in this case.
  - snovv_crash 4y ago
    Radar and ultrasound both give drastically less data than a simple 720p webcam. After postprocessing their output bandwidth is more similar to a 9-axis IMU than a camera.
    - 01100011 4y ago
      Yes and each time you process the incoming camera data in the neural network you have extra calculations due to the sensor fusion with another data source, regardless of its bitrate. Have you worked with deep learning much?