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Simultaneous Localization and Mapping is a key requirement for many practical applications in robotics. In this work, we present RESLAM, a novel edge-based SLAM system for RGBD sensors. Due to their sparse representation, larger convergence basin and stability under illumination changes, edges are a promising alternative to feature-based or other direct approaches. We build a complete SLAM pipeline with camera pose estimation, sliding window optimization, loop closure and relocalisation capabilities that utilizes edges throughout all steps. In our system, we additionally refine the initial depth from the sensor, the camera poses and the camera intrinsics in a sliding window to increase accuracy. Further, we introduce an edge-based verification for loop closures that can also be applied for relocalisation. We evaluate RESLAM on wide variety of benchmark datasets that include difficult scenes and camera motions and also present qualitative results. We show that this novel edge-based SLAM system performs comparable to state-of-the-art methods, while running in real-time on a CPU. RESLAM is available as open-source software.
|Proceedings of the International Conference on Robotics and Automation 2019
|Veröffentlicht - 20 Mai 2019
|IEEE International Conference on Robotics and Automation: ICRA 2019 - Palais des congrès de Montréal, Montreal, Kanada
Dauer: 20 Mai 2019 → 24 Mai 2019
|IEEE International Conference on Robotics and Automation
|20/05/19 → 24/05/19
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