Laser scanners, or laser radars (ladar), have been used for a number of years for mobile robot navigation and inspection tasks. Although previous scanners were sufficient for low speed applications, they often did not have the range or angular resolution necessary for mapping at the long distances. Many also did not provide an ample field of view with high accuracy and high precision. In this paper we will present the development of state-of-the-art, high speed, high accuracy, 3D laser radar technology. This work has been a joint effort between CMU and K2T and Z+F. The scanner mechanism provides an unobstructed 360/spl deg/ horizontal field of view, and a 70/spl deg/ vertical field of view. Resolution of the scanner is variable with a maximum resolution of approximately 0.06 degrees per pixel in both azimuth and elevation. The laser is amplitude-modulated, continuous-wave with an ambiguity interval of 52 m, a range resolution of 1.6 mm, and a maximum pixel rate of 625 kHz. This paper will focus on the design and performance of the laser radar and will discuss several potential applications for the technology. It reports on performance data of the system including noise, drift over time, precision, and accuracy with measurements. Influences of ambient light, surface material of the target and ambient temperature for range accuracy are discussed. Example data of applications will be shown and improvements will also be discussed.

We present a novel method for obstacle detection for automated highway environments. Laser range scanners have frequently been used for obstacle detection for mobile robots. Although most laser scanners provide intensity information in addition to range, laser intensity has been ignored by most researchers. We show that laser intensity, on its own, is sufficient (and better) for detecting obstacles at long ranges in mild terrain such as an automated highway.

Recent research in automated highway systems has ranged from low-level vision-based controllers to high-level route-guidance software. However there is currently no system for tactical-level reasoning. Such a system should address tasks such as passing cars, making exits on time, and merging into a traffic stream. Our approach to this intermediate-level planning combines a distributed reasoning system (PolySAPIENT) with a novel evolutionary optimization strategy (PBIL). PBIL automatically tunes PolySAPIENT module parameters in simulation by evaluating candidate modules on various traffic scenarios. Since the control interface to the simulated vehicles is identical to that on the Carnegie Mellon Navlab vehicles, modules developed using this process can be directly ported to existing hardware. This method is currently being applied to the automated highway system domain; it also generalizes to many complex robotics tasks where multiple interacting modules must simultaneously be configured without individual module feedback.

ELVIS (Eigenvectors for Land Vehicle Image System) is a road-following system designed to drive the CMU Navlabs. It is based on ALVINN, the neural network road-following system built by Dean Pomerleau at CMU. ELVIS is an attempt to more fully understand ALVINN and to determine whether it is possible to design a system that can rival ALVINN using the same input and output, but without using a neural network. Like ALVINN, ELVIS observes the road through a video camera and observes human steering response through encoders mounted on the steering column. After a few minutes of observing the human trainer, ELVIS can take control. ELVIS learns the eigenvectors of the image and steering training set via principal component analysis. These eigenvectors roughly correspond to the primary features of the image set and their correlations to steering. Road-following is then performed by projecting new images onto the previously calculated eigenspace. ELVIS architecture and experiments are discussed as well as implications for eigenvector-based systems and how they compare with neural network-based systems.