Abstract-This paper presents an algorithm for segmenting 3D point clouds. It extends terrain elevation models by incorporating two types of representations: (1) ground representations based on averaging the height in the point cloud, (2) object models based on a voxelisation of the point cloud. The approach is deployed on Riegl data (dense 3D laser data) acquired in a campus type of environment and compared against six other terrain models. Amongst elevation models, it is shown to provide the best fit to the data as well as being unique in the sense that it jointly performs ground extraction, overhang representation and 3D segmentation. We experimentally demonstrate that the resulting model is also applicable to path planning.
Although full 3D navigation and mapping is recognized as one of the most important challenges for autonomous navigation, the lack of robust sensors, providing 3D information in real time, has burdened the progress in this direction. This paper presents our ongoing work towards the deployment of an integrated sensing system for 3D mapping in outdoor environments. We first describe a 3D data acquisition architecture based on a standard 2D laser. Techniques for registering scans using a scan matching procedure and for estimating the errors are then introduced. We finally present results showing the performance of the proposed architecture in real outdoor environments by means of the integration of the 3D scans with dead reckoning and inertial measurement unit (IMU) information.
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