Probability driven approach for point cloud registration of indoor scene

Point cloud registration is a crucial step of localization and mapping with mobile robots or in object modeling pipelines. In this paper, we present a novel probability driven algorithm for point cloud registration of the indoor scene based on RGB-D images. Firstly, we extract the key points in RGB-D images and map the key points to 3D space as preprocessing. Then, we build the distance matrix and the difference matrix for each point cloud, respectively in scalarization and vectorization, to encode the structural proximity. And establish the corresponding point set by computing the matching probabilities. At last, we solve the transform matrix that aligns the source point cloud to the target point cloud. The entire registration framework consists of two phases: coarse registration based on the distance matrix (in scalarization) and fine registration based on the difference matrix (in vectorization). The two-phase registration strategy is able to greatly reduce the influence of inherent noise. Experiments demonstrate that our method outperforms in registration accuracy than the state-of-the-art methods. Furthermore, our method is more efficient than existing methods in computing speed because we utilize the location relationship between key points instead of point features. The source code is provided at our project website https://github.com/BeCoolGuy/Probability-Driven-Approach-for-Point-Cloud-Registration-of-Indoor-Scene.