Tracking naturally occurring indoor features in 2-D and 3-D with lidar range amplitude data

Sensor-data processing for the interpretation of a mobile robot's indoor environment, and the manipulation of this data for reliable localization, are still some of the most important issues in robotics. This article presents algorithms that determine the true position of a mobile robot, based on real 2-D and 3-D optical range and intensity data. We start with the physics of the particular type of sensor used, so that the extraction of reliable and repeatable information (namely, edge coordinates) can be determined taking into account the noise associated with each range sample and the possibility of optical multiple-path effects. Again, applying the physical model of the sensor the estimated positions of the mobile robot and the uncertainty in these positions are determined. We demonstrate real experiments using 2-D and 3-D scan data taken in indoor environments. To update the robot's position reliably, we address the problem of matching the information recorded in a scan to, first an a priori map, and second, to information recorded in previous scans, eliminating the need for an a priori map.