Open AccessAppearance‐based approach to hybrid metric‐topological simultaneous localisation and mapping
Author(s) -
Fernández Lorenzo,
Payá Luis,
Reinoso Oscar,
Jimenez Luis Miguel
Publication year - 2014
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2013.0086
Subject(s) - topological map , odometry , metric (unit) , computer vision , mobile robot , robot , artificial intelligence , computer science , simultaneous localization and mapping , metric map , node (physics) , omnidirectional antenna , position (finance) , omnidirectional camera , topology (electrical circuits) , monte carlo localization , global map , metric space , mathematics , engineering , mathematical analysis , telecommunications , operations management , structural engineering , finance , combinatorics , convex metric space , antenna (radio) , economics
In this study a unified framework to carry out the simultaneous localisation and mapping of a mobile robot combining metric and topological techniques is presented. The robot moves in a real indoor environment and the algorithm makes use of the information provided by an omnidirectional camera mounted on the robot and its internal odometry. The hybrid approach consists in constructing simultaneously two maps of the environment, one metric and other topological with relationships between them which are updated in each step. The robot goes through the environment to build up a map while continuously captures images. To build the topological map the most relevant information from the scenes is extracted using a global appearance descriptor. A new node is added to the map when the appearance between two images is sufficiently different. Also, the authors check if there is a loop closure with a previous node. At the same time, a metrical map of the environment is computed. With this aim, the authors estimate the position of the robot when it captures a new image using a Monte–Carlo algorithm. The authors show how it is possible to obtain a reasonable performance both in time and accuracy in an indoor environment, when the involved parameters are properly tuned.