Milo Henry Lovelace Knowles

abstract: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. In order 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.