For an autonomous vehicle, detecting and tracking other vehicles is a critical task. Determining the orientation of a detected vehicle is necessary for assessing whether the vehicle is a potential hazard. If a detected vehicle is moving, the orientation can be inferred from its trajectory, but if the vehicle is stationary, the orientation must be determined directly. In this paper, we focus on vision-based algorithms for determining vehicle orientation of vehicles in images. We train a set of Histogram of Oriented Gradients (HOG) classifiers to recognize different orientations of vehicles detected in imagery. We find that these orientation-specific classifiers perform well, achieving a 88% classification accuracy on a test database of 284 images. We also investigate how combinations of orientation-specific classifiers can be employed to distinguish subsets of orientations, such as driver's side versus passenger's side views. Finally, we compare a vehicle detector formed from orientation-specific classifiers to an orientation-independent classifier and find that, counter-intuitively, the orientation-independent classifier outperforms the set of orientation-specific classifiers.

This paper describes a system that semi-automatically builds a virtual world for remote operations by constructing 3-D models of a robot's work environment. With a minimum of human interaction, planar and quadric surface representations of objects typically found in man-made facilities are generated from laser rangefinder data. The surface representations are used to recognize complex models of objects in the scene. These object models are incorporated into a larger world model that can be viewed and analyzed by the operator, accessed by motion planning and robot safeguarding algorithms, and ultimately used by the operator to command the robot through graphical programming and other high level constructs. Limited operator interaction, combined with assumptions about the robots task environment, make the problem of modeling and recognizing objects tractable and yields a solution that can be readily incorporated into many telerobotic control schemes.

Presents a terrain mapping system for walking robots that constructs quantitative models of surface geometry, The accuracy of the constructed maps enables safe, power-efficient locomotion over the natural, rugged terrain found on planetary surfaces. The mapping system acquires range images with a laser rangefinder, preprocesses and stores the images, and constructs elevation maps from them at arbitrary resolutions, in arbitrary reference frames. To quantify performance in terms of accuracy, timing, and memory utilization, the authors conducted extensive tests in natural, rugged terrain, producing hundreds of millions of map points. The results indicate that the mapping system 1) is one of the few that can handle extremely rugged terrain, and 2) exhibits a high degree of real-world robustness due to its aggressive detection of image-based errors and in its compensation for time-varying errors.>

We describe the perception system for the Ambler, an autonomous, legged mobile robot that operates in rugged environments. The Ambler's perception system uses 3-D laser range images to construct elevation maps of the local terrain, processing hundreds of images and thousands of elevation points during a walking experiment. This paper presents the design, implementation, and analysis of a perception system to meet the needs of long-duration walking, emphasizing issues of perforumnce, accuracy, and reliability.

The Ambler is an autonomous orthogonal walking robot that operates in rugged environments. The mechanism has two stacks and six circulating legs. On top of the Ambler is mounted a laser scanner to obtain 3D data of the local terrain. It is designed for planetary exploration. During the course of walking experiments unexpected shortcomings in the robot's perception system were noted. Unusual perception sensor behavior, random noise sources, and interaction of the perception system with other system components caused both minor errors and large failures in the system. This paper analyzes the sources of the perception system errors, presents solutions to these problems, and proposes a design for a more robust perception system.>

CHIMERA is a real-time computing environment used in the Reconfigurable Modular Manipulator System project. CHIMERA, which is both a hardware and software environment, allows rapid development and implementation of real-time control programs. It provides a C/Unix-flavored concurrent programming environment for a Motorola 68020 multiprocessor hardware configuration connected to a Sun workstation. CHIMERA has been implemented using commercial hardware in conjunction with a sophisticated, locally developed software package, resulting in a reliable, reasonably priced, and easily duplicated system. CHIMERA is currently being ported for real-time control of the CMU Direct Drive Arm II. The authors describe the implementation and capabilities of the CHIMERA environment and illustrate how these features are used in robot control applications.>