ICRA 2011 Paper Abstract

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Paper TuP1-InteracInterac.36

Li, Yangming (Institute of Intelligence Machines, Chinese Academy of Sciences), Olson, Edwin (University of Michigan)

Structure Tensors for General Purpose LIDAR Feature Extraction

Scheduled for presentation during the Poster Sessions "Interactive Session II: Systems, Control and Automation" (TuP1-InteracInterac), Tuesday, May 10, 2011, 13:40−14:55, Hall

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 March 30, 2020

Keywords Mapping, Localization, SLAM

Abstract

The detection of features from Light Detection and Ranging (LIDAR) data is a fundamental component of feature- based mapping and SLAM systems. Classical approaches are often tied to specific environments, computationally expensive, or do not produce precise features. We describe a general purpose feature detector that is not only efficient, but also applicable to virtually any environment.

Our method shares its mathematical foundation with feature detectors from the computer vision community, where structure tensor based methods have been successful. Our resulting method is capable of identifying stable and repeatable features at a variety of spatial scales, and produces uncertainty estimates for use in a state estimation algorithm. We verify the proposed method on standard datasets, including the Victoria Park dataset and the Intel Research Center dataset.

 

 

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