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Many autonomous systems require the ability to perceive and understand motion in a dynamic environment. We present a novel algorithm that estimates this motion from raw LIDAR data in real-time without the need for segmentation or model-based tracking. The sensor data is first used to construct an occupancy grid. The foreground is then extracted via a learned background filter. Using the filtered occupancy...
This paper reports on an optical visual fiducial system developed for relative-pose estimation of two ships at sea. Visual fiducials are ubiquitous in the robotics literature, however none are specifically designed for use in outdoor lighting conditions. Blooming of the CCD causes a significant bias in the estimated pose of square tags that use the outer corners as point correspondences. In this paper,...
This paper reports on a fast multiresolution scan matcher for vehicle localization in urban environments for self-driving cars. State-of-the-art approaches to vehicle localization rely on observing road surface reflectivity with a three-dimensional (3D) light detection and ranging (LIDAR) scanner to achieve centimeter-level accuracy. However, these approaches can often fail when faced with adverse...
This paper reports on the problem of map-based visual localization in urban environments for autonomous vehicles. Self-driving cars have become a reality on roadways and are going to be a consumer product in the near future. One of the most significant road-blocks to autonomous vehicles is the prohibitive cost of the sensor suites necessary for localization. The most common sensor on these platforms,...
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