NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance

Geoffrey Pascoe, Will Maddern, Michael Tanner, Pedro Pinies, Paul Newman

CVPR 2017

doi:10.1109/CVPR.2017.158 /cvpr/2017/pascoe2017cvpr-nidslam/

Abstract

We propose a direct monocular SLAM algorithm based on the Normalised Information Distance (NID) metric. In contrast to current state-of-the-art direct methods based on photometric error minimisation, our information-theoretic NID metric provides robustness to appearance variation due to lighting, weather and structural changes in the scene. We demonstrate successful localisation and mapping across changes in lighting with a synthetic indoor scene, and across changes in weather (direct sun, rain, snow) using real-world data collected from a vehicle-mounted camera. Our approach runs in real-time on a consumer GPU using OpenGL, and provides comparable localisation accuracy to state-of-the-art photometric methods but significantly outperforms both direct and feature-based methods in robustness to appearance changes.

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Text

Pascoe et al. "NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance." Conference on Computer Vision and Pattern Recognition, 2017. doi:10.1109/CVPR.2017.158

Markdown

[Pascoe et al. "NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance." Conference on Computer Vision and Pattern Recognition, 2017.](https://mlanthology.org/cvpr/2017/pascoe2017cvpr-nidslam/) doi:10.1109/CVPR.2017.158

BibTeX

@inproceedings{pascoe2017cvpr-nidslam,
  title     = {{NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance}},
  author    = {Pascoe, Geoffrey and Maddern, Will and Tanner, Michael and Pinies, Pedro and Newman, Paul},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2017},
  doi       = {10.1109/CVPR.2017.158},
  url       = {https://mlanthology.org/cvpr/2017/pascoe2017cvpr-nidslam/}
}