Simultaneous Localization and Mapping using RGB-D cameras has been a fertile research topic in the latest decade, due to the suitability of such sensors for indoor robotics. In this paper we propose a direct RGB-D SLAM algorithm with state-of-the-art accuracy and robustness at a los cost. Our experiments in the RGB-D TUM dataset [34] effectively show a better accuracy and robustness in CPU real time than direct RGB-D SLAM systems that make use of the GPU. The key ingredients of our approach are mainly two. Firstly, the combination of a semi-dense photometric and dense geometric error for the pose tracking (see Figure 1), which we demonstrate to be the most accurate alternative. And secondly, a model of the multi-view constraints and their errors in the mapping and tracking threads, which adds extra information over other approaches. We release the open-source implementation of our approach 1 . The reader is referred to a video with our results 2 for a more illustrative visualization of its performance.
Kintinuous @cite_9 builds on KinectFusion and uses a rolling cyclical buffer that shifts the volume as the camera is moving, hence not being restricted to small workspaces. It also includes loop closing and pose graph optimization for global consistency.
@cite_9
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In this paper we propose an extension to the KinectFusion approach which enables both SLAM-graph optimization, usually required on large looping routes, as well as discovery of semantic information in the form of object detection and localization. Global optimization is achieved by incorporating the notion of keyframe into a KinectFusion-style approach, thus providing the system with the ability to explore large environments and maintain a globally consistent map. Moreover, we integrate into the system our recent object detection approach based on a new Semantic Bundle Adjustment paradigm, thereby achieving joint detection, tracking and mapping. Although our current implementation is not optimized for real-time operation, the principles and ideas set forth in this paper can be considered a relevant contribution towards a Semantic KinectFusion system.