Real-time robot motion planning using rasterizing computer graphics hardware
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
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
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Markov Localization using Correlation
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Exploring artificial intelligence in the new millennium
Atlas: a framework for large scale automated mapping and localization
Atlas: a framework for large scale automated mapping and localization
Fast Laser Scan Matching using Polar Coordinates
International Journal of Robotics Research
Robust and efficient robotic mapping
Robust and efficient robotic mapping
Progress toward multi-robot reconnaissance and the MAGIC 2010 competition
Journal of Field Robotics
WAMbot: Team MAGICian's entry to the Multi Autonomous Ground-robotic International Challenge 2010
Journal of Field Robotics
Information-theoretic compression of pose graphs for laser-based SLAM
International Journal of Robotics Research
Exploration and mapping with autonomous robot teams
Communications of the ACM
Inference on networks of mixtures for robust robot mapping
International Journal of Robotics Research
Journal of Intelligent and Robotic Systems
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Scan matching, the problem of registering two laser scans in order to determine the relative positions from which the scans were obtained, is one of the most heavily relied-upon tools for mobile robots. Current algorithms, in a trade-off for computational performance, employ heuristics in order to quickly compute an answer. Of course, these heuristics are imperfect: existing methods can produce poor results, particularly when the prior is weak. The computational power available to modern robots warrants a re-examination of these quality vs. complexity trade-offs. In this paper, we advocate a probabilistically-motivated scan-matching algorithm that produces higher quality and more robust results at the cost of additional computation time. We describe several novel implementations of this approach that achieve real-time performance on modern hardware, including a multiresolution approach for conventional CPUs, and a parallel approach for graphics processing units (GPUs). We also provide an empirical evaluation of our methods and several contemporary methods, illustrating the benefits of our approach. The robustness of the methods make them especially useful for global loop-closing.