This shows the 121-km path taken between Oxford in the upper left and London in the bottom right. We compute visual estimates for 89.4% of this trajectory and fall back on inertial sensing for the remainder.

Abstract – In this paper we describe a relative approach to simultaneous localisation and mapping, based on the insight that a continuous relative representation can make the problem tractable at large scales. First, it is well known that bundle adjustment is the optimal non-linear least- squares formulation for this problem, in that its maximum-likelihood form matches the definition of the Cramer–Rao lower bound. Unfortunately, computing the maximum-likelihood solution is often prohibitively expensive: this is especially true during loop closures, which often necessitate adjusting all parameters in a loop. In this paper we note that it is precisely the choice of a single privileged coordinate frame that makes bundle adjustment costly, and that this expense can be avoided by adopting a completely relative approach. We derive a new relative bundle adjustment which, instead of optimising in a single Euclidean space, works in a metric space defined by a manifold. Using an adaptive optimization strategy, we show experimentally that it is possible to solve for the full maximum-likelihood solution incrementally in constant time, even at loop closure. Our approach is, by definition, everywhere locally Euclidean, and we show that the local Euclidean estimate matches that of traditional bundle adjustment. Our system operates online in realtime using stereo data, with fast appearance-based loop closure detection. We show results on over 850,000 images that indicate the accuracy and scalability of the approach, and process over 330 GB of image data into a relative map covering 142 km of Southern England. To demonstrate a baseline sufficiency for navigation, we show that it is possible to find shortest paths in the relative maps we build, in terms of both time and distance. Query images from the web of popular landmarks around London, such as the London Eye or Trafalgar Square, are matched to the relative map to provide route planning goals.

  • [PDF] G. Sibley, C. Mei, I. Reid, and P. Newman, “Vast Scale Outdoor Navigation Using Adaptive Relative Bundle Adjustment,” International Journal of Robotics Research, vol. 29, iss. 8, pp. 958-980, 2010.
    [Bibtex]
    @article{SibleyEtAlIJRR2010,
    Author = {Gabe Sibley and Christopher Mei and Ian Reid and Paul Newman},
    Journal = {International Journal of Robotics Research},
    Keywords = {Relative SLAM},
    Month = {July},
    Number = {8},
    Owner = {ashley},
    Pages = {958 - 980},
    Pdf = {http://www.robots.ox.ac.uk/~mobile/Papers/2010IJRR_sibley.pdf},
    Timestamp = {2011.07.25},
    Title = {Vast Scale Outdoor Navigation Using Adaptive Relative Bundle Adjustment},
    Volume = {29},
    Year = {2010}}
13-km path taken around Oxford with inset showing New College portion (a separate 2.2-km trajectory around New College is also shown.  This sequence is ideal for testing loop-closure detection.

13-km path taken around Oxford with inset showing New College portion (a separate 2.2-km trajectory around New College is also shown. This sequence is ideal for testing loop-closure detection.

Plot of Relative SLAM estimates on the New College dataset with loop-closure accounting.

Plot of Relative SLAM estimates on the New College dataset with loop-closure accounting.