Loop-Closure Detection in Urban Scenes for Autonomous Robot Navigation

Relocalization is a vital process for autonomous robot navigation, typically running in the background of sequential localization and mapping to detect loops in the robot's trajectory. Such loop-closure detections enable corrections for drift accumulated during the estimation processes and even recovery from complete localization failures. In this work, we present a novel approach loosely integrated with a keyframe-based SLAM system to perform loop-closure detection in urban scenarios for autonomous robot navigation. Generating a mesh of the current robot's surroundings in real-time using monocular and inertial cues, the proposed method estimates the most salient plane in the current view, enabling the creation of the corresponding orthophoto for this plane. Evaluating image similarity on orthophotos forms a much better conditioned problem for relocalization, minimizing effects from viewpoint changes. Employing binary image descriptors and tests on their relative constellation in the image, the proposed approach exhibits robustness also to illumination and situational variations common in real scenes, overall resulting to significant improvement in loop-closure detection performance in urban scenes with respect to the state of the art.