Robust online map merging system using laser scan matching and omnidirectional vision

Abstract

This paper describes a probabilistic online map merging system for a single mobile robot. It performs intermittent exploration by fusing laser scan matching and omnidirectional vision. Moreover, it can also be adapted to a multi-robot system for large scale environments. Map merging is achieved by means of a probabilistic Haar-based place recognition system using omnidirectional images and is capable of discriminating new and previously visited locations in the current or previously collected maps. This dramatically reduces the search space for laser scan matching. The combination of laser range finding and omnidirectional vision is very attractive because they reinforce one another when there is sufficient structure and visual information in the environment. In other cases, they complement one another, leading to improved robustness of the system. This is the first system to combine a probabilistic Haar-based place recognition system using omnidirectional images with laser range finding to merge maps. The proposed system is also algorithmically simple, efficient and does not require any offline processing. Experimental results of the approach clearly illustrate that the proposed system can perform both online map merging and exploration robustly using a single robot configuration in a real indoor lab environment.