Stable keypoints selection for 2D LiDAR based place recognition with map data reduction

This paper presents a new feature based approach for place recognition using 2D LiDAR (Light Detection And
Ranging) data. The main contribution lies in the mapping process. It includes a keypoint selection strategy to model
places with persistent keypoints from concatenated LiDAR scans. Our objective is to achieve map data reduction
while maintaining good place recognition performace. LiDAR scans are concatenated with a registration algorithm
and keypoints are extracted from each scan. Based on a regular grid, our approach measures the occurrence of sim-
ilar keypoints in each region of interest defined by a grid cell. Only keypoints with occurrences beyond a threshold,
qualified as stable keypoints, are kept in the place model called submap. The environment is therefore modeled as a
collection of submaps, which constitutes the global map. Place recognition consists in submap matching followed
by a two steps geometric verification. In the first stage, optimal parameters are set using corrected data. Mapping
parameters satisfy six criteria among which is the spatial distribution distance, which represents another contri-
bution of our work. It gives a measure on how well keypoints are distributed in space. Place recognition optimal
parameters are set through global localization. In the second stage, the approach is evaluated using raw data in the
contexts of global localization, and loop closure detection in a SLAM framework. The results obtained using pop-
ular real data sets show that our approach achieves significant map data reduction (up to 92%) while maintaining
good place recognition performance, comparable to state-of-the-art methods.