Enhanced Monte Carlo Localization with Visual Place Recognition for Robust Robot Localization

被引：19

作者：

Perez, Javier ^{[1
]}

Caballero, Fernando ^{[2
]}

Merino, Luis ^{[1
]}

机构：

[1] Univ Pablo de Olavide, Seville, Spain

[2] Univ Seville, Seville, Spain

来源：

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS | 2015年 / 80卷 / 3-4期

关键词：

Monte Carlo localization; Long-term localization; Robust localization; Crowded environment; BAGS;

D O I：

10.1007/s10846-015-0198-y

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes extending Monte Carlo Localization methods with visual place recognition information in order to build a robust robot localization system. This system is aimed to work in crowded and non-planar scenarios, where 2D laser rangefinders may not always be enough to match the robot position within the map. Thus, visual place recognition will be used in order to obtain robot position clues that can be used to detect when the robot is lost and also to reset its positions to the right one. The paper presents experimental results based on datasets gathered with a real robot in challenging scenarios.

引用

页码：641 / 656

页数：16

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