Noise Tolerance of Linear vs Non-Linear LiDAR Based Ego-Motion Drift Correction Methods

Corvin-Petrut Cobârzan, Catalin-Cosmin Golban, Sergiu Nedevschi

Abstract: We have previously proposed a linear approach for reducing the global drift of a video-based frame-to-frame trajectory estimation method by correcting it at selected points in time based on the alignment of past and current 3D LiDAR measurements (see [7]). In this paper we assess the tolerance to noise of a series of methods derived from the one previously proposed, this time using both linear and non-linear optimization methods to calculate the correction transform. We generate synthetic datasets with various noise pollution levels and assess the performance of each method under investigation in recovering artificially induced odometry estimation errors.