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高翔的无监督回环检测方法
This paper is concerned of the loop closure
detection problem for visual simultaneous localization and
mapping systems.We propose a novel approach based on the
stacked denoising auto-encoder (SDA), a multi-layer neural
network that autonomously learns an compressed representation
from the rawinput data in an unsupervisedway.Different
with the traditional bag-of-words based methods, the deep
network has the ability to learn the complex inner structures
in image data, while no longer needs to manually design the
visual features. Our approach employs the characteristics of
the SDA to solve the loop detection problem. The workflow
of training the network, utilizing the features and computing
the similarity score is presented. The performance of SDA
is evaluated by a comparison study with Fab-map 2.0 using
data from open datasets and physical robots. The results show
that SDA is feasible for detecting loops at a satisfactory precision
and can therefore provide an alternative way for visual
SLAM systems.
2018-09-03
FAB-MAP 2.0算法对应论文
We describe a new formulation of appearance-only
SLAM suitable for very large scale navigation. The system navigates
in the space of appearance, assigning each new observation
to either a new or previously visited location, without reference to
metric position. The system is demonstrated performing reliable
online appearance mapping and loop closure detection over a
1,000km trajectory, with mean filter update times of 14 ms. The
1,000km experiment is more than an order of magnitude larger
than any previously reported result. The scalability of the system
is achieved by defining a sparse approximation to the FAB-MAP
model suitable for implementation using an inverted index. Our
formulation of the problem is fully probabilistic and naturally
incorporates robustness against perceptual aliasing. The 1,000km
data set comprising almost a terabyte of omni-directional and
stereo imagery is available for use, and we hope that it will serve
as a benchmark for future systems.
2018-09-03
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