Depth data fusion for simultaneous localization and mapping — RGB-DD SLAM

Abstract

This paper presents an approach to data fusion from multiple depth sensors with different principles of range measurements. This concept is motivated by the observation that depth sensors exploiting different range measurement techniques have also distinct characteristics of the uncertainty and artifacts in the obtained depth images. Thus, fusing the information from two or more measurement channels allows us to mutually compensate for some of the unwanted effects. The target application for our combined sensor is Simultaneous Localization and Mapping (SLAM). We demonstrated that fusing depth data from two sources in the convex optimization framework yields better results in feature-based 3-D SLAM, than the use of individual sensors for this task. The experimental part is based on data registered with a calibrated rig comprising ASUS Xtion Pro Live and MESA SwissRanger SR-4000 sensors, and ground truth trajectories obtained from a motion capture system. The results of sensor trajectory estimation are demonstrated in terms of the ATE and RPE metrics, widely adopted by the SLAM community.