In this paper we present a complete SLAM system for RGB-D cameras, namely RGB-iD SLAM. The presented approach is a dense direct SLAM method with the main characteristic of working with the depth maps in inverse depth parametrisation for the routines of dense alignment or keyframe fusion. The system consists in 2 CPU threads working in parallel, which share the use of the GPU for dense alignment and keyframe fusion routines. The first thread is a front-end operating at frame rate, which processes every incoming frame from the RGB-D sensor to compute the incremental odometry and integrate it in a keyframe which is changed periodically following a covisibility-based strategy. The second thread is a back-end which receives keyframes from the front-end. This thread is in charge of segmenting the keyframes based on their structure, describing them using Bags of Words, trying to find potential loop closures with previous keyframes, and in such case perform pose-graph optimisation for trajectory correction. In addition, our system allows is able to compute the odometry both with unregistered and registered depth maps, allowing to use customised calibrations of the RGB-D sensor. As a consequence in the paper we also propose a detailed calibration pipeline to compute customised calibrations for particular RGB-D cameras. The experiments with our approach in the TUM RGB-D benchmark datasets show results superior in accuracy to the state-of-the-art in many of the sequences. The code has been made available on-line for research purposes this https URL.
This paper presents a new dense method to compute the odometry of a free-flying range sensor in real time. The method applies the range flow constraint equation to sensed points in the temporal flow to derive the linear and angular velocity of the sensor in a rigid environment. Although this approach is applicable to any range sensor, we particularize its formulation to estimate the 3-D motion of a range camera. The proposed algorithm is tested with different image resolutions and compared with two state-of-the-art methods: generalized iterative closest point (GICP) [1] and robust dense visual odometry (RDVO) [2] . Experiments show that our approach clearly overperforms GICP which uses the same geometric input data, whereas it achieves results similar to RDVO, which requires both geometric and photometric data to work. Furthermore, experiments are carried out to demonstrate that our approach is able to estimate fast motions at 60 Hz running on a single CPU core, a performance that has never been reported in the literature. The algorithm is available online under an open source license so that the robotic community can benefit from it.