The outdoor perception problem is a major challenge for driver-assistance and autonomous vehicle systems. While these systems can often employ active sensors such as sonar, radar, and lidar to perceive their surroundings, the state of standard traffic lights can only be perceived visually. By using a prior map, a perception system can anticipate and predict the locations of traffic lights and improve detection of the light state. The prior map also encodes the control semantics of the individual lights. This paper presents methods for automatically mapping the three dimensional positions of traffic lights and robustly detecting traffic light state onboard cars with cameras. We have used these methods to map more than four thousand traffic lights, and to perform onboard traffic light detection for thousands of drives through intersections.

Registering multiple sets of 3D range data is a crucial capability for robots. The standard method for matching two sets of range data is to convert the ranges to a point cloud representation, and then use on of the many variants of iterative closest point (ICP). We present a set of alternative methods for matching 3D range scans based on a different data representation: evidence grid maps. Evidence grids are robust to noise and variations in point density, can incorporate an indefinite number of ranges, and explicitly encode empty as well as occupied space. While 3D evidence grids can be huge when naively implemented, we use an optimized octree data structure to efficiently store sparse volumetric maps. To register a series of range scans, we build an evidence grid map for each scan, and then register them together using a several different methods. The first two methods are based on a 3D extension of the classic 2D Lucas-Kanade template matching method, and differ only in whether we match a single large region, or multiple small regions that are selected heuristically. Our third method involves extracting surfaces from the evidence grids, and then running ICP to register the surfaces. We demonstrate our methods and compare them to ICP using two datasets collected by two different subterranean robots.

This paper describes the application of a RaoBlackwellized Particle Filter to the problem of simultaneous localization and mapping onboard a hovering autonomous underwater vehicle. This vehicle, called DEPTHX, equipped with a large array of pencil-beam sonars for mapping, and autonomously explore a system of flooded tunnels associated with the Zacaton sinkhole in Tamaulipas, Mexico. Due to the three-dimensional nature of the tunnels, we describe an extension of traditional two dimensional evidence grids to three dimensions. In May 2005, we collected a sonar data set in Zacaton. We present successful SLAM results using both the real-world data and simulated data