Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Approximate nearest neighbor queries in fixed dimensions
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
A column approximate minimum degree ordering algorithm
ACM Transactions on Mathematical Software (TOMS)
6D SLAM—3D mapping outdoor environments: Research Articles
Journal of Field Robotics
Exactly Sparse Delayed-State Filters for View-Based SLAM
IEEE Transactions on Robotics
iSAM: Incremental Smoothing and Mapping
IEEE Transactions on Robotics
Calibrating an outdoor distributed camera network using laser range finder data
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Self-calibration for a 3D laser
International Journal of Robotics Research
Online semantic mapping of urban environments
SC'12 Proceedings of the 2012 international conference on Spatial Cognition VIII
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We present an approach to the problem of 3D map building in urban settings for service robots, using three-dimensional laser range scans as the main data input. Our system is based on the probabilistic alignment of 3D point clouds employing a delayed-state information-form SLAM algorithm, for which we can add observations of relative robot displacements efficiently. These observations come from the alignment of dense range data point clouds computed with a variant of the iterative closest point algorithm. The datasets were acquired with our custom built 3D range scanner integrated into a mobile robot platform. Our mapping results are compared to a GIS-based CAD model of the experimental site. The results show that our approach to 3D mapping performs with sufficient accuracy to derive traversability maps that allow our service robots navigate and accomplish their assigned tasks on a urban pedestrian area.