EKF-based 3D SLAM for Structured Environment Reconstruction

This paper presents the extension and experimental validation of the widely used EKF1-based SLAM2 algorithm to 3D space. It uses planar features extracted probabilistically from dense three-dimensional point clouds generated by a rotating 2D laser scanner. These features are represented in compliance with the Symmetries and Perturbation model (SPmodel) in a stochastic map. As the robot moves, this map is updated incrementally while its pose is tracked by using an Extended Kalman Filter. After showing how three-dimensional data can be generated, the probabilistic feature extraction method is described, capable of robustly extracting (infinite) planes from structured environments. The SLAM algorithm is then used to track a robot moving through an indoor environment and its capabilities in terms of 3D reconstruction are analyzed.

Presented at:
None, Edmonton, Canada, August 2-6

 Record created 2006-12-07, last modified 2018-01-27

External link:
Download fulltext
Rate this document:

Rate this document:
(Not yet reviewed)