-
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011, San Francisco, CA, USA, September 25-30, 2011; 01/2011
-
IEEE International Conference on Robotics and Automation, ICRA 2010, Anchorage, Alaska, USA, 3-7 May 2010; 01/2010
-
[show abstract]
[hide abstract]
ABSTRACT: Scan-matching is a technique that can be used for building accurate maps and estimating vehicle motion by comparing a sequence of point cloud measurements of the environment taken from a moving sensor. One challenge that arises in mapping applications where the sensor motion is fast relative to the measurement time is that scans become locally distorted and difficult to align. This problem is common when using 3D laser range sensors, which typically require more scanning time than their 2D counterparts. Existing 3D mapping solutions either eliminate sensor motion by taking a “stop-and-scan” approach, or attempt to correct the motion in an open-loop fashion using odometric or inertial sensors. We propose a solution to 3D scan-matching in which a continuous 6DOF sensor trajectory is recovered to correct the point cloud alignments, producing locally accurate maps and allowing for a reliable estimate of the vehicle motion. Our method is applied to data collected from a 3D spinning lidar sensor mounted on a skid-steer loader vehicle to produce quality maps of outdoor scenes and estimates of the vehicle trajectory during the mapping sequences.
Robotics and Automation, 2009. ICRA '09. IEEE International Conference on; 06/2009
-
Field and Service Robotics, Results of the 7th International Conference, FSR 2009, Cambridge, Massachusetts, USA, 14-16 July 2009; 01/2009
-
I. J. Robotic Res. 01/2008; 27:667-691.
-
Experimental Robotics, The Eleventh International Symposium, ISER 2008, July 13-16, 2008, Athens, Greece; 01/2008
-
[show abstract]
[hide abstract]
ABSTRACT: We present and evaluate two variants of an algorithm for simultaneously segmenting and modeling a mixed-density unstructured 3D point cloud by ellipsoidal (Gaussian) region growing. The base algorithm merges initial el-lipsoids into larger ellipsoidal segments with a minimum spanning tree algorithm. The vari-ants differ only in the merge criterion used—a threshold on a generalised distance measure de-fined on the merge candidates. The first variant (shape-distance) considers the relative shape, orientation and position of the ellipsoids, and can grow regions across missing or sparse data, whilst the second (density-distance) attempts to maintain a good fit to the data by setting a minimum sample density threshold on the merged ellipsoid. Adjusting the threshold in each case changes the quality and degree of segmentation achieved. The threshold param-eter is tuned by minimising Akaike's Informa-tion Criterion (AIC) with respect to the thresh-old value. Experiments show that thresholds selected in this way lead to low complexity models and are stable across different environ-ments. The shape-distance measure segments large-scale structures more readily than the density-distance measure, but leads to higher AIC scores, and higher model complexity.
-
[show abstract]
[hide abstract]
ABSTRACT: We address the place recognition problem, which we define as the problem of establishing whether an observed location has been previously seen, and if so, determining the transformation aligning the current observations to an existing map. In the contexts of robot navigation and mapping, place recognition amounts to globally localizing a robot or map segment without being given any prior estimate. An efficient method of solving this problem involves first selecting a set of keypoints in the scene which store an encoding of their local region, and then utilizing a sublinear-time search into a database of keypoints previously generated from the global map to identify places with common features. We present an algorithm to embed arbitrary keypoint descriptors in a reduced-dimension metric space, in order to frame the problem as an efficient nearest neighbor search. Given that there are a multitude of possibilities for keypoint design, we propose a general methodology for comparing keypoint location selection heuristics and descriptor models that describe the region around the keypoint. With respect to selecting keypoint locations, we introduce a metric that encodes how likely it is that the keypoint will be found in the presence of noise and occlusions during mapping passes. Metrics for keypoint descriptors are used to assess the distinguishability between the distributions of matches and non-matches and the probability the correct match will be found in an approximate k-nearest neighbors search. Verification of the test outcomes is done by comparing the various keypoint designs on a kilometers-scale place recognition problem. We apply our design evaluation methodology to three keypoint selection heuristics and six keypoint descriptor models. A full place recognition system is presented, including a series of match verification algorithms which effectively filter out false positives. Results from city-scale and long-term mapping problems illustrate our approach for both offline and online SLAM, map merging, and global localization and demonstrate that our algorithm is able to produce accurate maps over trajectories of hundreds of kilometers.
Robotics and Autonomous Systems.
