Abstract— This paper presents a novel semantic categorization method for 3D point cloud data using supervised, multi-class Gaussian Process (GP) classification. In contrast to other approaches, and particularly Support Vector Machines, which probably are the most used method for this task to date, GPs have the major advantage of providing informative uncertainty estimates about the resulting class labels. As we show in experiments, these uncertainty estimates can either be used to improve the classification by neglecting uncertain class labels or – more importantly – they can serve as an indication of the under-representation of certain classes in the training data. This means that GP classifiers are much better suited in a life- long learning framework, where not all classes are represented initially, but instead new training data arrives during the operation of the robot.

  • [PDF] R. Paul, R. Triebel, D. Rus, and P. Newman, “Semantic Categorization of Outdoor Scenes with Uncertainty Estimates using Multi-Class Gaussian Process Classification,” in Proc. of the International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal, 2012.
    [Bibtex]

    @inproceedings{PaulIROS2012,
    Address = {Vilamoura, Portugal},
    Author = {Rohan Paul and Rudolph Triebel and Daniela Rus and Paul Newman},
    Booktitle = {Proc. of the International Conference on Intelligent Robots and Systems (IROS)},
    Keywords = {Semantic Categorization},
    Month = {October},
    Pdf = {http://www.robots.ox.ac.uk/~mobile/Papers/2012IROS_rp.pdf},
    Title = {Semantic Categorization of Outdoor Scenes with Uncertainty Estimates using Multi-Class Gaussian Process Classification},
    Year = {2012}}