Abstruct-We present a terrain mapping system for walking robots that constructs quantitative models of surface geometry. The accuracy of the constructed maps enables safe, powerefficient 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, we 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.
Abstract-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 orientationspecific 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 orientationspecific classifiers to an orientation-independent classifier and find that, counter-intuitively, the orientation-independent classifier outperforms the set of orientation-specific classifiers.
The Autonomous Planetary Rover Project at Carnegie Melion University is investigating the use of geometric information obtained from terrain elevation maps for mobile robot planning and control. We review how surface geometry has been characterized by surface roughness parameters, and why several of these parameten must be combined to form a vector roughness measurement. Next we propose a techniqne to localize and extract the intrinsic roughness from terrain elevation maps, and show how this can be used to chmcterizt terrain.
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