NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance

Geoffrey Pascoe, Will Maddern, Michael Tanner, Pedro Pinies, Paul Newman; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1435-1444

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.

Related Material


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[bibtex]
@InProceedings{Pascoe_2017_CVPR,
author = {Pascoe, Geoffrey and Maddern, Will and Tanner, Michael and Pinies, Pedro and Newman, Paul},
title = {NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}