We conduct a comprehensive performance comparison of descriptor-based data association methods for object-level SLAM. Based on our findings, we design a visual bag-of-words descriptor from Oriented FAST and Rotated BRIEF descriptors extracted from object bounding boxes. To disambiguate between perceptually similar objects and improve the efficiency of our algorithm, we incorporate a heuristic for filtering associations based on geometric information from the robot pose and object map. We show that our bag-of-words approach with geometric filtering outperforms the precision, recall, and runtime of baseline descriptor matching methods on 21 challenging driving sequences from the KITTI Tracking Dataset.

Working as UROP student in the Robust Robotics Group for the past two years has given me a lot of exposure to current research in autonomous robotics, especially perception. I’m excited to continue exploring this area of robotics through SuperUROP and publish or present results.