Efficient Monocular SLAM Using a Structure-Driven Mapping
I am working with the problem of simultaneous localization and mapping using a single hand-held camera as unique sensor. Specifically, looking at how to extract a richer representation of the environment using the map generated by the system and the visual information obtained with the camera. Therefore, I am aiming to extract higher level structure by using the point-based map provided by the SLAM operation. At the moment I am investigating the problem of how to extract planar surfaces into the environment. Once they are extracted the next challenge implies how to use them as part of the estimation. This approach shoud be similar to the one using CAD models with the difference of the planar structure being obtained on the fly and without any prior knowledge of the scene.
Efficient Visual Odometry Using a Structure-Driven Temporal Map
International Conference on Robotics and Automation. St Paul-Minnesota, USA. May, 2012
We describe a method for visual odometry using a
single camera based on an EKF framework. Previous work has
shown that filtering based approaches can achieve accuracy per-
formance comparable to that of optimisation methods providing
that large numbers of features are used. However, computa-
tional requirements are signicantly increased and frame rates
are low. We address this by employing higher level structure - in
the form of planes - to efficiently parameterise features and so
reduce the filter state size and computational load. Moreover, we
extend a 1-point RANSAC outlier rejection method to the case
of features lying on planes. Results of experiments with both
simulated and real-world data demonstrate that the method
is effective, achieving comparable accuracy whilst running at
significantly higher frame rates.
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Unifying Planar and Point Mapping in Monocular SLAM
British Machine Vision Conference. Aberystwyth, UK. September, 2010
Mapping planar structure in vision-based SLAM can increase robustness and significantly improve efficiency of map representation. However, previous systems have implemented planar mapping as an auxiliary process on top of point based mapping, leading to delayed initialisation and increased feature management overhead. We address this by introducing a unified mapping framework based on a common parameterization in which both planar and point features are mapped directly, as and when appropriate according to scene structure. Specifically, no distinction is made between points and planes at initialisation - the 'best' representation emerges after matching has progressed - hence minimizing delay and making the detection of planar structure implicit in the method, avoiding the need for an additional process. We demonstrate the approach within an EKF monocular SLAM system and show its potential for efficient and robust mapping over large areas in both indoor and outdoor environments, including examples of fast relocalisation.
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Efficiently Increasing Map Density in Visual SLAM Using Planar Features with Adaptive Measurements
British Machine Vision Conference. London, UK. September, 2009
Point based visual SLAM suffers from a trade off between map density and computational efficiency. With too few mapped points, tracking range is restricted and resistance to occlusion is reduced, whilst expanding the map to give dense representation significantly increases computation. We address this by introducing higher order structure into the map using planar features. The parameterisation of structure allows frame by frame adaptation of measurements according to visibility criteria, increasing the map density without increasing computational load. This facilitates robust camera tracking over wide changes in viewpoint at significantly reduced computational cost. Results of real-time experiments with a hand-held camera demonstrate the effectiveness of the approach.
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Appearance Based Extraction of Planar Structure in Monocular SLAM
To be presented in the Scandinavian Conference on Image Analysis. Oslo, Norway. June, 2009
This work concerns the building of enhanced scene maps during real-time
monocular SLAM. Specifically, we present a novel algorithm for detecting
and estimating planar structure in a scene based on both appearance and
geometric information. We adopt a hypothesis
testing framework, in which the validity of planar patches within a
triangulation of the point based scene map are assessed against an
appearance metric. A key contribution is that the metric incorporates the
uncertainties available within the SLAM filter through the use of
a test statistic assessing error distribution against predicted covariances,
hence maintaining a coherent probabilistic formulation. Experimental results indicate that the approach
is effective, having good detection and discrimination properties, and leading to
convincing planar feature representations.