# LiDAR Feature Extraction

I'd like to extract "features" of an environment scanned with a 2D LiDAR.

I tried to create a temporary occupancy map to extract "corners" with the Harris Corner Detector. However LiDAR data is noisy, it creates false corners even after applying a Gaussian filter on the map. I wonder if there are more reliable algorithms for this.

Another method I tried is to use raw data output of the LiDAR, i.e. the $$(r,\theta)$$ vector, and calculate its first and second derivatives and choose "abrupt changes" as landmarks. This has a major drawback, it is harder to track landmarks when LiDAR position changes since $$(r,\theta)$$ vectors are totally different.

I appreciate any help. Thanks.

• What do you mean by the (r,θ) vector? Aug 11, 2021 at 19:20

In the very simple case you can just apply a low-pass filter to each of your cells. Like that: $$p[t] = a*p[t-1] + (1-a)*I(occupied)$$ where I is an indicator function which is set to 1 if current lidar measurement shows that cell is occupied and to 0 otherwise.