ICRA 2012 Paper Abstract

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Paper WeC07.3

Wilson Vieira, Antonio (Universidade Federal de Minas Gerais), Drews Jr, Paulo (Federal University of Rio Grande (FURG)), Campos, Mario Montenegro (Universidade Federal de Minas Gerais)

Efficient Change Detection in 3D Environment for Autonomous Surveillance Robots based on Implicit Volume

Scheduled for presentation during the Regular Session "Environment Mapping" (WeC07), Wednesday, May 16, 2012, 15:00−15:15, Meeting Room 7 (Remnicha)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on October 24, 2017

Keywords Range Sensing, Surveillance Systems, Intrusion Detection, Identification and Security

Abstract

The ability to detect changes in the environment is an essential trait for robots commissioned to work in several applications. In surveillance, for instance, a robot needs to detect meaningful changes in the environment which is achieved by comparing current sensory data with previously acquired information from the environment. The large amount of sensory data, which are often complex and very noisy, explains the inherent difficulty of this task. As an attempt to tackle this hard problem, we present an efficient method to automatically segment 3D data, corrupted with noise and outliers, into an implicit volume bounded by a surface. The method makes it possible to efficiently apply Boolean operations to 3D data in order to detect changes and to update existing maps. We show that our approach is powerful, albeit simple, with linear time complexity. The method has been validated through several trials using mobile robots operating in real environments and their performance was compared to another state-of-art algorithm. Experimental results demonstrate the performance of the proposed method, both in accuracy and computational cost.

 

 

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