Hybrid vision-based SLAM coupled with moving object tracking

Abstract

In this paper we propose a hybrid vision-based SLAM and moving objects tracking (vSLAMMOT) approach. This approach tightly combines two key methods: A superpixel-based segmentation to detect moving objects and a Rao-Blackwellized Particle Filter to estimate a stereo-vision-based SLAM posterior. Most successful methods perform vision-based SLAM (vSLAM) and track moving objects independently. However, we pose both vSLAM and moving object tracking as a single correlated problem to leverage the performance. Our approach estimates the relative camera motion using the previous tracking result, and then detects moving objects from the estimated camera motion recursively. Moving superpixels are detected by a Markov Random Field (MRF) model which uses spatial and temporal information of the moving objects. We demonstrate the performance of the proposed approach for vSLAMMOT using both synthetic and real datasets and compare the performance with other methods.