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 software1.

In this work, we present a real-time robust edge-based visual odometry framework for RGBD sensors (REVO). Even though our method is independent of the edge detection algorithm, we show that the use of state-of-the-art machine-learned edges gives significant improvements in terms of robustness and accuracy compared to standard edge detection methods. In contrast to approaches that heavily rely on the photo-consistency assumption, edges are less influenced by lighting changes and the sparse edge representation offers a larger convergence basin while the pose estimates are also very fast to compute. Further, we introduce a measure for tracking quality, which we use to determine when to insert a new key frame. We show the feasibility of our system on real-world datasets and extensively evaluate on standard benchmark sequences to demonstrate the performance in a wide variety of scenes and camera motions. Our framework runs in real-time on the CPU of a laptop computer and is available online.