Title: Scale Drift-Aware Large Scale Monocular SLAM
Author: Hauke Strasdat, J.M.M. Montiel, Andrew J. Davison
Abstract—State of the art visual SLAM systems have recently
been presented which are capable of accurate, large-scale and
real-time performance, but most of these require stereo vision.
Important application areas in robotics and beyond open up
if similar performance can be demonstrated using monocular
vision, since a single camera will always be cheaper, more
compact and easier to calibrate than a multi-camera rig.
With high quality estimation, a single camera moving through
a static scene of course effectively provides its own stereo
geometry via frames distributed over time. However, a classic
issue with monocular visual SLAM is that due to the purely
projective nature of a single camera, motion estimates and map
structure can only be recovered up to scale. Without the known
inter-camera distance of a stereo rig to serve as an anchor, the
scale of locally constructed map portions and the corresponding
motion estimates is therefore liable to drift over time.
In this paper we describe a new near real-time visual SLAM
system which adopts the continuous keyframe optimisation approach
of the best current stereo systems, but accounts for
the additional challenges presented by monocular input. In
particular, we present a new pose-graph optimisation technique
which allows for the efficient correction of rotation, translation
and scale drift at loop closures. Especially, we describe the
Lie group of similarity transformations and its relation to the
corresponding Lie algebra. We also present in detail the system’s
new image processing front-end which is able accurately to track
hundreds of features per frame, and a filter-based approach
for feature initialisation within keyframe-based SLAM. Our
approach is proven via large-scale simulation and real-world
experiments where a camera completes large looped trajectories.
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