Sparse Direct Robot Localization Method Based on RGB-D Camera

In order to address the current challenges associated with feature-based RGB-D SLAM, this paper puts forward a novel sparse direct localization algorithm. Contributions of the paper are manifold. Firstly, the proposed algorithm achieves rapid feature-point detection as well as camera pose estimation through a minimization strategy of the photometric error associated with image coupling. Secondly, a computational optimization scheme is put forward for the proposed algorithm such that key-frames are selected adaptively using a spatial domain framework which monitors the robot's motion in real-time, and applies a Nearest Neighbor algorithm towards loop closure detection. Finally, the proposed algorithm achieves robot pose estimation and optimization in real-time using a General Framework for Graph Optimization (g2o) strategy. The performance of the proposed algorithm is verified through live robotic experimental evaluation. The achieved results suggest that the scheme attains significantly high localization accuracies with low RMSE within a range of 25 meters. For scenarios where the camera remains fixed, RMSE reaches up to 1% within a range of 29.6 meters. Furthermore, the proposed scheme achieves localization speeds of up to 45 fps, demonstrating superior real-time capabilities, and addressing computational drawbacks associated with state-of-the-art.