The dominant architecture for mobile robot perception uses sensors on-board the robot, providing only a first-person perspective on the environment. This work describes a novel mobile robot system that uses an environment-based sensor network, providing a powerful third-person perspective on the environment. In our previous work (1999), we described an algorithm that computes a real-time spatial-temporal occupancy map from the multiple video streams of the sensor network. We also described a novel path-planning algorithm based upon this system. In this work we describe a novel motion control loop that is based upon tracking the mobile robot in the occupancy map. Tracking in the fused perceptual space of the sensor network provides several advantages over tracking individually in a set of raw sensor spaces. We demonstrate a prototype system operating in several dynamic scenarios.

We describe an algorithm to fuse intensity data from multiple video cameras to create a spatial-temporal occupancy map. The camera layout is assumed to resemble a security video network. The occupancy map is a two-dimensional raster image, uniformly distributed in the floor-plane. Each map pixel contains a binary value, signifying whether the designated floorspace is empty or occupied. Our algorithm requires only one difference and one look-up table operation to determine each pixel's effect upon the map. This brevity of operations allows the spatial occupancy map to be temporally computed at real-time video rates. We demonstrate our algorithm operating in several dynamic scenarios.