Laser reflectance feature assisted accurate extrinsic calibration for non-repetitive scanning LiDAR and camera systems.

Non-repetitive scanning Light Detection And Ranging(LiDAR)-Camera systems are commonly used in autonomous navigation industries, benefiting from their low-cost and high-perception characteristics. However, due to the irregular scanning pattern of LiDAR, feature extraction on point cloud encounters the problem of non-uniformity distribution of density and reflectance intensity, accurate extrinsic calibration remains a challenging task. To solve this problem, this paper presented an open-source calibration method using only a printed chessboard.

Feature homogenization strategy using optical flow for vision-related simultaneous localization and mapping in a complex environment.

Vision-related state estimation usually extracts multiple feature points from images captured by the camera. In this paper, we propose a robust feature homogenization method for resolving the problem of feature clustering. The proposed method deduced the depth of feature points from optical flow magnitude, and the homogenization of feature points was acquired by adaptively enforcing the minimum distance between neighboring feature points.