Incremental map building using an occupancy grid for an autonomous monocular robot

This paper centres on the problem of estimating the location of features in the environment within a map building framework for a mobile robot with a single camera. Most of the existing approaches to feature location from vision data have been developed by a combination of matching and geometric triangulation. Triangulation is used to obtain equations of reconstruction or structure-frommotion. In contrast, our approach avoids the matching problem by looking for evidence of the location of features on an occupancy grid. Similar to map building via sonar methods, we gather evidence of the trace of potential position of features. Thus, maxima in the occupancy grid define robust feature positions. Preliminary results show that our approach can obtain accurate results in real time. The approach can handle noise in the feature detection and in the robot position. The integration of vision data over time reduces the covariance of the estimates.