Performance Analysis of Unmanned Vehicle Positioning and Obstacle Mapping
Abstract: As unmanned ground vehicles take on more and more intelligent tasks, determination of potential obstaclesand accurate estimation of their position become critical for successful navigation and path planning. The performanceanalysis of obstacle mapping and unmanned vehicle positioning in outdoor environments is the subject of this paper.Recently, the National Institute of Standards and Technology’s (NIST) Intelligent Systems Division has been a part of theDefense Advanced Research Project Agency LAGR (Learning Applied to Ground Robots) Program. NIST's objective forthe LAGR Project is to insert learning algorithms into the modules that make up the NIST 4D/RCS (FourDimensional/Real-Time Control System) standard reference model architecture which has been successfully applied tomany intelligent systems. We detail world modeling techniques used in the 4D/RCS architecture and then analyze thehigh precision maps generated by the vehicle world modeling algorithms as compared to ground truth obtained from anindependent differential GPS system operable throughout most of the NIST campus. This work has implications, notonly for outdoor vehicles but also, for indoor automated guided vehicles where future systems will have more and moreonboard intelligence requiring non-contact sensors to provide accurate vehicle and object positioning
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