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Project Information - C18501

Project Name: C18501
Project Description: Tiger Taxi
Project track:
DPM (Planning) term:
SD1 (Starting) term:
SD2 (Ending) term:
Description: Self-driving of autonomous robots and vehicles require agents to learn and maintain accurate maps for safe and reliable operation. We use a variant of pose-graph Simultaneous Localization and Mapping (SLAM) to integrate multiple sensors for autonomous navigation in an urban environment. Our methods efficiently and accurately localize the agent across a map generated from different sensors across different periods of time. To incorporate a priori localization data, we propose an amendment of the pose-graph error function by accounting for error between observations and publicly available building geometry. We fuse data derived from heterogeneous sensor modalities using extended Kalman filters to increase invariance to dynamic environmental factors, such as weather, luminance, and occlusion. To discriminate traversable terrain, we employ a deep segmentation network whose predictions increase confidence of a LiDAR-generated cost map. Path planning is accomplished using the Timed-Elastic-Band algorithm on the persistent map created through SLAM. We evaluate our method in varying environmental conditions on a large university campus and show the efficacy of the sensor and map fusion.
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default node: C18501/public/Home
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