Using Naturally Salient Regions for SLAM with 3D Laser Data

We consider the task of processing 3D laser data for use in the Simultaneous Localization and Mapping Problem. The motivation for using 3D data comes in part from the impracticality of relying on 2D laser scanners when the vehicle operates on undulating terrain and in part from a desire to produce 3D maps of arbitrary, a priori unknown environments. We use an information-theoretic derived measure of local saliency to partition the raw 3D data stream into spatially distinct point-clusters. These clusters are natural features in measurement space that capture the geometry of intrinsically interesting surface patches. In common with “scan-matching” methods in SE2, the SE3 relationship between consecutive vehicle poses is calculated using an iterative point-wise registration scheme operating on the reduced data set. The saliency driven decimation process not only substantially reduces the computational burden of registration but also provides the registration process with data that is geometrically diverse. This characteristic improves registration performance. We present initial results showing our methods working on both outdoor and indoor data.