Network Uncertainty Informed Semantic Feature Selection for Visual SLAM

29 Nov 2018Pranav GantiSteven L. Waslander

In order to facilitate long-term localization using a visual simultaneous localization and mapping (SLAM) algorithm, careful feature selection can help ensure 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 semantic segmentation and neural network uncertainty into the feature selection pipeline... (read more)

PDF Abstract

Evaluation Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.