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In this paper, we propose an integrated system to detect and track a single operator that can switch off and on when it leaves and (re-)enters the scene. Our method is based on a set-valued Bayes-optimal state estimator that integrates RGB-D detections and image-based classification to improve tracking results in severe clutter and under long-term occlusion. The classifier is trained in two stages:...
In this paper we describe an approach for detection and pose estimation of colored objects with only few or no textural features. The approach consists of two separate stages. First, we perform vision-based object detection and hypothesis filtering. Then, we estimate and validate the object's pose in 3-D laser scans. For object detection we integrate image segmentation results from multiple viewpoints...
The Probability Hypothesis Density (PHD) filter is an efficient formulation of multi-target state estimation that circumvents the combinatorial explosion of the multi-target posterior by operating on single-target space without maintaining target identities. In this paper, we propose a multi-target tracker based on the PHD filter that provides instantaneous state estimation and delayed decision on...
Detection and tracking of pedestrians is an essential task for autonomous outdoor robots. Modern 3D laser range finders provide a rich and detailed 360 degree picture of the environment. Unstructured environments pose a difficult scenario where a variety of objects with similar shape to a human like shrubs or small trees occur. Especially in combination with partial occlusions, sensor noise, and concussions...
Autonomous navigation in unstructured environments is a complex task and an active area of research in mobile robotics. Unlike urban areas with lanes, road signs, and maps, the environment around our robot is unknown and unstructured. Such an environment requires careful examination as it is random, continuous, and the number of perceptions and possible actions are infinite.We describe a terrain classification...
The detection and tracking of moving vehicles is a necessity for collision-free navigation. In natural unstructured environments, motion-based detection is challenging due to low signal to noise ratio. This paper describes our approach for a 14 km/h fast autonomous outdoor robot that is equipped with a Velodyne HDL-64E S2 for environment perception. We extend existing work that has proven reliable...
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