Adaptive compensation visual odometry in dynamic scenarios

Abstract

In dynamic scenarios, dynamic participants break the static assumptions of the visual odometry (VO) algorithm. Hence, dynamic participants are typically removed and only static participants are used as motion references. When semantic information is used to eliminate participants with dynamic semantic labels in the scene, the dynamic degree of the scene affects system robustness and poses estimation accuracy. In this paper, we propose a VO system that employs adaptive compensation for semantic information and utilizes an adaptive compensation model for sparse static feature map construction to make the compensation process a flexible, independent thread. Moreover, a layered extraction fusion framework is proposed to uniformly sample equi-probability feature points both globally and locally. This framework combines multi-level weights of scene mapping and spatial-temporal priority information of self-organization. Finally, in the candidate pixel extraction stage, system efficiency is improved through region matching candidate pixel detection, extraction and fusion semantic constraints. Experiments are done by using the TUM RGBD datasets. Comparisons with many conventional visual mileage calculation methods reveal that the absolute and relative trajectory errors of camera motion are significantly reduced in most scenes with different dynamic degrees. Thus, compared to previous algorithms, the proposed algorithm is more robust and precise in dynamic scenes.