THESIS
2020
xix, 137 pages : illustrations (some color) ; 30 cm
Abstract
Visual-inertial SLAM has been a contemporary research theme with various emerging
commercial applications like robot navigation, augmented reality, 3D mapping etc. With
the advent of several SLAM systems the theory of multiview geometry has been put to
practical use. A typical SLAM system consists of several sub-systems including: visual-odometry,
sensor-fusion, place recognition backend, place recognition frontend, posegraph
solver. For a successful commercial deployment of SLAM algorithm it is important that
the SLAM system be failsafe. In this thesis, we present several techniques for fail-safety
of a SLAM system. We start by proposing an edge based visual-odometry method. The
advantage of edge based visual odometry over traditional methods based on corner features
and optic...[
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Visual-inertial SLAM has been a contemporary research theme with various emerging
commercial applications like robot navigation, augmented reality, 3D mapping etc. With
the advent of several SLAM systems the theory of multiview geometry has been put to
practical use. A typical SLAM system consists of several sub-systems including: visual-odometry,
sensor-fusion, place recognition backend, place recognition frontend, posegraph
solver. For a successful commercial deployment of SLAM algorithm it is important that
the SLAM system be failsafe. In this thesis, we present several techniques for fail-safety
of a SLAM system. We start by proposing an edge based visual-odometry method. The
advantage of edge based visual odometry over traditional methods based on corner features
and optical flow is that such methods also work well in featureless human built
environment like corridors. Another advantage is that, the proposed method has a large
convergence basin which allows for more reliable odometry computation under large motion
or low frame rates. Next we present a learning based whole-image descriptor for loop
detection. We demonstrated much higher recall rates compared to existing bag-of-visual-words
based loop detection methods. Unlike previous loop detection methods which only
evaluate their methods on fronto-parallel scenes, we tested our on datasets involving large
viewpoint difference. In addition to higher recall, our method involves an order of magnitude
less model storage size compared to bag-of-words dictionary and also an order
of magnitude lesser FLOPS (floating point operations) making it suitable for a realtime
SLAM system. We also propose a robust feature matching scheme and a local bundle
optimization based computation for reliably estimating relative pose at loop detections.
Unlike some existing works which merge trajectories from multiple runs offline, we develop
a pose graph solver which is able to keep track of multiple co-ordinate systems, identify
and recover from kidnaps live and in realtime. Extensive online experimental results are
presented throughout the thesis. We conclude by proposing future research opportunities.
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