Robust Simultaneous Localization and Mapping via Loop Closure Optimization

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SLAM (Simultaneous Localization And Mapping) has been an extremely active and fast-evolving field for the past decades. It attempts to solve the fundamental problem in truly autonomous systems: how to extract information from data and fuse the information to create a map of the environment, which facilitates the subsequent decision making process. Despite the extensive works and vast advances, long-term operation remained an open problem due to the brittleness of current SLAM systems. Perceptual aliasing, where different places appear similar to the sensor, prevents the loop closure detection as well as map estimation from achieving reliable performance over long periods of time. The main goal of this project is to develop a pose graph optimization algorithm that is resilient to spurious loop closure detections. ARA Robotics, a Montreal-based company that specializes in aerial robotics and software design, is currently expanding its offering to cover SLAM solutions, and enhance the navigation and mapping capability of their robots, using the RTAB-MAP framework. ARA Robotics is interested in research on a robust pose graph optimization algorithm that enhances the performance of long-term SLAM operation. The project will contribute to a software product for SLAM that will contribute to the development of Canada's Unmanned Air Vehicle (UAV) industry and robotics industry in general. This research project will apply state-of-the-art work on SLAM to highly sought-after civilian applications for UAVs. This will have direct relevance to the Canadian economy.