May 2019
tl;dr: Use PointNet++ to perform semantic segmentation of radar point cloud.
Overall impression
The radar point cloud are very sparse, and is usually 2D, lacking the elevation information. However it has one extra important dimension – Doppler.
Key ideas
- 4D point cloud data (radial distance, azimuth angle, ego-motion compensated Doppler, Radar Cross Section/RCS).
- Eliminates needs to cluster point cloud and extract features from cluster.
- Grid maps (including occupancy grid map or RCS maps) are good for static scenes but not for moving objects.
- Feature propagation (FP) module to propagate sparse label to dense neighborhood.
- Five classes:
ped, ped groups, cyclists, cars, trucks. All others are static, including clutter (previously with label garbage).
cars are easily confused with trucks.ped and ped groups are hard to differentiate, as there are noise in human annotation as well.- precision for
cars are not good, only ~68%. Most FP should be static.
- Ego-motion compensated Doppler has a very large effect on model performance.
Technical details
- For autonomous cars, radar and lidar sensors supplement cameras to maintain functional safety. These additional sensors should not only work complementary but also redundantly.
- In MSG (multiscale grouping module), only spatial info is considered for grouping. Only spatial info (x, y) are used in the grouping.
- Sparse data:
- Even at coarse resolution of 1m x 1m, at most 6% of the grid will have non-zero values.
- Only 2% to 3% of all points are non-static objects.
- Each point in moving object is dropped out with random prob from [0, 0.3].
- 500 ms worth of data is accumulated. But only 3072 data points are used (if more, then static points are dropped; if less, then points are resampled). During inference, every 3072 points were passed though network in the chronological order so no over- or under-sampling.
- Moving vs Doppler: Doppler is not absolute indicator of moving objects. Many static objects also have non-zero Doppler due to error in odometry, sensor misalignment, time sync error, mirror effects or other sensor artifacts. On the other hand, bottom of a rotating car wheel or pedestrian walking radially also does not have doppler effect.
Notes
- Feature propagation module should be very useful in propagating sparse labels to dense data. Need to read PointNet++ again.
- Plotting Range-Cross range map with Doppler as color legend helps quite a lot in human annotation.
- Doppler signal needs to be motion compensated.