Triangulation-based fusion of sonar data with application in robot pose tracking

In this paper a sensor fusion scheme, called triangulation-based fusion (TBF) of sonar data, is presented. This algorithm delivers stable natural point landmarks, which appear in practically all indoor environments, i.e., vertical edges like door posts, table legs, and so forth. The landmark precision is in most cases within centimeters. The TBF algorithm is implemented as a voting scheme, which group sonar measurements that are likely to have hit a mutual object in the environment. The algorithm has low complexity and is sufficiently fast for most mobile robot applications. As a case study, we apply the TBF algorithm to robot pose tracking. The pose tracker is implemented as a classic extended Kalman filter, which use odometry readings for the prediction step and TBF data for measurement updates. The TBF data is matched to pre-recorded reference maps of landmarks in order to measure the robot pose. In corridors, complementary TBF data measurements from the walls are used to improve the orientation and position estimate. Experiments demonstrate that the pose tracker is robust enough for handling kilometer distances in a large scale indoor environment containing a sufficiently dense landmark set.