SLAMfusion: Fusing SLAM Methods for Improved Robustness

Abstract

There are multiple approaches for SLAM, but we found the the ones implemented in ROS had problems when a robot drove over small obstacles. This paper presents a proposal to make a more robust SLAM by running three SLAM methods in parallel and using their information to produce a better estimate of the robot's surroundings. The proposed method defines its output by making the three methods vote for the value of each pixel in the map. To deal with the increased computational complexity, the method is implemented in the GPU. The performed experiments show that our method shows smaller error than any of the three fused methods alone both when there are ground obstacles that induce map errors and also when no obstacles are present, thus presenting in fact an increase in robustness.