Probabilistic Surfel Fusion for Dense LiDAR Mapping

Chanoh Park, Soohwan Kim, Peyman Moghadam, Clinton Fookes, Sridha Sridharan; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2418-2426

With the recent development of high-end LiDARs, more and more systems are able to continuously map the environment while moving and producing spatially redundant information. However, none of the previous approaches were able to effectively exploit this redundancy in a dense LiDAR mapping problem. In this paper, we present a new approach for dense LiDAR mapping using probabilistic surfel fusion. The proposed system is capable of reconstructing a high-quality dense surface element (surfel) map from spatially redundant multiple views. This is achieved by a proposed probabilistic surfel fusion along with a geometry considered data association. The proposed surfel data association method considers surficial resolution as well as high measurement uncertainty which makes the mapping system being able to control surface resolution without introducing space digitization. The proposed fusion method successfully suppresses the map noise level by considering a Bayesian filtering framework.