Real-Time Graph-Based SLAM with Occupancy Normal Distributions Transforms

Simultaneous Localization and Mapping (SLAM) is one of the basic problems in mobile robotics. While most approaches are based on occupancy grid maps, Normal Distributions Transforms (NDT) and mixtures like Occupancy Normal Distribution Transforms (ONDT) have been shown to represent sensor measurements more accurately. In this work, we slightly re-formulate the (O)NDT matching function such that it becomes a least squares problem that can be solved with various robust numerical and analytical non-linear optimizers. Further, we propose a novel global (O)NDT scan matcher for loop closure. In our evaluation, our NDT and ONDT methods are able to outperform the occupancy grid map based ones we adopted from Google's Cartographer implementation.