Visual SLAM with Network Uncertainty Informed Feature Selection.
Year: 2018
Type: preprint
Abstract: In order to facilitate long-term localization using a visual simultaneouslocalization and mapping (SLAM) algorithm, careful feature selection is
required such that reference points persist over long durations and the runtime
and storage complexity of the algorithm remain consistent. We present SIVO
(Semantically Informed Visual Odometry and Mapping), a novel
information-theoretic feature selection method for visual SLAM which
incorporates machine learning and neural network uncertainty into the feature
selection pipeline. Our algorithm selects points which provide the highest
reduction in Shannon entropy between the entropy of the current state, and the
joint entropy of the state given the addition of the new feature with the
classification entropy of the feature from a Bayesian neural network. This
feature selection strategy generates a sparse map suitable for long-term
localization, as each selected feature significantly reduces the uncertainty of
the vehicle state and has been detected to be a static object (building,
traffic sign, etc.) repeatedly with a high confidence. The KITTI odometry
dataset is used to evaluate our method, and we also compare our results against
ORB_SLAM2. Overall, SIVO performs comparably to ORB_SLAM2 (average of 0.17%
translation error difference, 6.2 x 10^(-5) deg/m rotation error difference)
while reducing the map size by 69%.
Source: arXiv (Cornell University)
Authors: Pranav Ganti, Steven L. Waslander
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2902415727