This is a drivable road regions (obstacle detection) algorithm found in what appears to be a git repo leaked from an autonomous vehicle company in 2017. The repo was available through one or more Tor hidden services for several years.
The lidar code (in an almost-C dialect of C++, a ROS node) appears to be written for the Velodyne HDL-32E. It operates in a series of stages, each stage refining the output of the previous stage. This algorithm is in the second stage. It is the primary obstacle detection method, with the other methods making only small improvements.
The leaked code uses a column-major matrix of points and it explicitly handles NaNs (the no-return points). We've rewritten it to use a much more cache-efficient row-major matrix layout and a conditional that will ignore the NaN points without explicit testing.
This is an amazingly effective method of obstacle detection, considering its simplicity. We've tested this using a VLP-32C lidar, user manual here if you're interested:
The lidar code (in an almost-C dialect of C++, a ROS node) appears to be written for the Velodyne HDL-32E. It operates in a series of stages, each stage refining the output of the previous stage. This algorithm is in the second stage. It is the primary obstacle detection method, with the other methods making only small improvements.
The leaked code uses a column-major matrix of points and it explicitly handles NaNs (the no-return points). We've rewritten it to use a much more cache-efficient row-major matrix layout and a conditional that will ignore the NaN points without explicit testing.
This is an amazingly effective method of obstacle detection, considering its simplicity. We've tested this using a VLP-32C lidar, user manual here if you're interested:
https://icave2.cse.buffalo.edu/resources/sensor-modeling/VLP...