Abstract— This paper describes a probabilistic framework
for appearance based navigation and mapping using spatial and
visual appearance data. Like much recent work on appearance
based navigation we adopt a bag-of-words approach in which
positive or negative observations of visual words in a scene are
used to discriminate between already visited and new places.
In this paper we add an important extra dimension to the
approach. We explicitly model the spatial distribution of visual
words as a random graph in which nodes are visual words
and edges are distributions over distances. Care is taken to
ensure that the spatial model is able to capture the multi-modal
distributions of inter-word spacing and account for sensor
errors both in word detection and distances. Crucially, these
inter-word distances are viewpoint invariant and collectively
constitute strong place signatures and hence the impact of using
both spatial and visual appearance is marked. We provide
results illustrating a tremendous increase in precision-recall
area compared to a state-of-the-art visual appearance only


  • [PDF] R. Paul and P. Newman, “FAB-MAP 3D: Topological Mapping with Spatial and Visual Appearance,” in Proc. IEEE International Conference on Robotics and Automation (ICRA’10), Anchorage, Alaska, 2010, pp. 2649-2656.
    author = {Rohan Paul and Paul Newman},
    title = {FAB-MAP 3D: Topological Mapping with Spatial and Visual Appearance},
    booktitle = {Proc. {IEEE} International Conference on Robotics and Automation (ICRA'10)},
    year = {2010},
    pages = {2649-2656},
    address = {Anchorage, Alaska},
    month = {May},
    note = {05},
    pdf = {http://www.robots.ox.ac.uk/~mobile/Papers/1751.pdf},
    keywords = {Topological Mapping and Loop Closing With Vision and Laser and FABMAP},