SP-SLAM: Surfel-Point Simultaneous Localization and Mapping

In this article, a novel method for simultaneous localization and mapping (SLAM) named surface elements (surfel) point SLAM (SP-SLAM) is proposed using an RGB-D camera. The key idea of SP-SLAM is to use not only keypoints but also surfels as features to cope with both high texture and low texture environments. By decomposing a surface into a small number of surfels, the method can represent spacious environments using a relatively small amount of memory. To optimize the poses of points, surfels, and cameras altogether, new objective functions are proposed, and a new bundle adjustment using these objective functions is developed. The proposed SP-SLAM runs in real time on a central processing unit as in other featurebased visual SLAM methods but works better than them not only in high texture but also in low texture environments, overcoming well-known drawbacks of feature-based visual SLAM with degradation in low texture environments. The proposed method is applied to benchmark datasets and its effectiveness is demonstrated by comparing against those of previous methods in terms of localization accuracy.