Combination of Planes and Image Edges for a Visual Odometer for Weakly Textured Structured Environments

Camera trajectory estimation has generated a lot of interest during the last decades, especially for robotic positioning. As it is well-known, outdoors positioning mainly relies on GPS, whereas visual odometry is typically preferred indoors. Nevertheless, when the environment is weakly textured, visual odometers typically degrade in performance. Facing this situation, this paper develops and tests a novel visual odometer that combines image edges and planar information to estimate the trajectory of an RGB-D camera in environments that lack texture. We also present a plane matching method based on a graph matching technique. To conclude, a comparison involving the proposed odometer for two well-known datasets and other visual odometers and SLAM systems is reported. This comparison shows that our method outperforms the others for indoor poorly-textured environments, while similar performance is obtained for indoor highly-textured environments.