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Paper TuP1-InteracInterac.45

McManus, Colin (University of Toronto), Furgale, Paul Timothy (University of Toronto), Barfoot, Timothy (University of Toronto)

Towards Appearance-Based Methods for Lidar Sensors

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 Computer Vision for Robotics and Automation, Localization, Visual Navigation

Abstract

Cameras have emerged as the dominant sensor modality for localization and mapping in three-dimensional, unstructured terrain, largely due to the success of sparse, appearance-based techniques, such as visual odometry. However, the Achilles' heel for all camera-based systems is their dependence on consistent ambient lighting, which poses a serious problem in outdoor environments that lack adequate or consistent light, such as the Moon. Actively illuminated sensors on the other hand, such as a light detection and ranging (lidar) device, use their own light source to illuminate the scene, making them a favourable alternative in light-denied environments. The purpose of this paper is to demonstrate that the largely successful appearance-based methods traditionally used with cameras can be applied to laser-based sensors, such as a lidar. We present two experiments that are vital to understanding and enabling appearance-based methods for lidar sensors. In the first experiment, we explore the stability of a representative keypoint detection and description algorithm on both camera images and lidar intensity images collected over a 24 hour period. In the second experiment, we validate our approach by implementing visual odometry based on sparse bundle adjustment on a sequence of lidar intensity images.

 

 

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