Building a map for robot path planning by fusing video images and laser rangefinder data

This thesis describes algorithms that fuse the data from a single video camera and a laser rangefinder. By merging information from these sensors, the algorithms build a digital map of the local environment for a robot to use in navigation. The video image is segmented, and these segments are used to construct a vector scan for the laser rangefinder. The vector scan is a sequence of straight line segments that is created by a greedy algorithm; the range measurements along this vector scan are used to build the map. To build or augment the map, the range measurements are projected to the floor plane and then accumulated in a histogram. This histogram is then summed in an uncertainty region around each point, and points with probability mass greater than one half are marked as obstacle points. The algorithms are tested on an example scenario incorporating actual video images and simulated laser rangefinder data. The map produced by the algorithms shows a good representation of the obstacles without any false alarms