Adaptive algorithm for the SLAM design with a RGB-D camera

Simultaneous localization and mapping (SLAM) is a well-known problem in the field of autonomous mobile robotics where a robot needs to localize itself in unknown environments by processing onboard sensors without external referencing systems such as Global Positioning System (GPS). In this work, we present a visual feature-based SLAM, which is able to produce high-quality tridimensional maps in real time with a low-cost RGB-D camera such as the Microsoft Kinect. It is suitable for future planning or common robot navigation tasks. First, a comprehensive performance evaluation of the combination of different state-of-the-art feature detectors and descriptors is presented. The main purpose behind these evaluations is to determine the best detector-descriptor algorithm combination to use for robot navigation. Second, we use the Iterative Closest Point (ICP) algorithm to get the relative motion between consecutive frames and then refine the pose estimate following the composition rule. However, the spatial distribution and resolution of depth data affect the performance of 3D scene reconstruction based on ICP. Due to this, we propose an adaptive architecture which computes the pose estimate from the most reliable measurements in a given environment. We evaluate our approach extensively on common available benchmark datasets. The experimental results demonstrate that our system can robustly deal with challenging scenarios while being fast enough for online applications.