ICRA 2011 Paper Abstract


Paper WeA105.1

Tong, Chi Hay (University of Toronto), Barfoot, Timothy (University of Toronto)

Batch Heterogeneous Outlier Rejection for Feature-Poor SLAM

Scheduled for presentation during the Regular Sessions "SLAM I" (WeA105), Wednesday, May 11, 2011, 08:20−08:35, Room 3G

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 14, 2020

Keywords SLAM, Localization, Mapping


In this paper, the problem of outliers in a batch alignment problem (given heterogeneous measurements and sparse features) is considered. The conventional approach from the field of computer vision, pairwise RANSAC, is shown to be inappropriate for this scenario, which motivates the need for a new method. To address this problem, the heterogeneous measurements are compared in a common currency using their respective scaled measurement innovations. Furthermore, a family of three algorithms for classifying outliers given a hypothesis model are presented, each having its own balance between speed and accuracy. These classification criteria are then incorporated through iterative reclassification in a batch alignment framework, providing a robust estimate for localization and mapping. Lastly, statistical validation is obtained through a large set of simulated trials.



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