Abstract
We present a new collision detection algorithm to perform contact computations between noisy point cloud data. Our approach takes into account the uncertainty that arises due to discretization error and noise, and formulates collision checking as a two-class classification problem. We use techniques from machine learning to compute the collision probability for each point in the input data and accelerate the computation using stochastic traversal of bounding volume hierarchies. We highlight the performance of our algorithm on point clouds captured using PR2 sensors as well as synthetic data sets, and show that our approach can provide a fast and robust solution for handling uncertainty in contact computations.
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Acknowledgments
This work was supported in part by ARO Contract W911NF-10-1-0506, NSF grants 0917040, 0904990, and 1000579, and Willow Garage. The dataset generated using Kinect RGB-D cameras was provided to us by Dieter Fox and Peter Henry at the University of Washington.
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Pan, J., Chitta, S., Manocha, D. (2017). Probabilistic Collision Detection Between Noisy Point Clouds Using Robust Classification. In: Christensen, H., Khatib, O. (eds) Robotics Research . Springer Tracts in Advanced Robotics, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-319-29363-9_5
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DOI: https://doi.org/10.1007/978-3-319-29363-9_5
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