Simultaneous localization and mapping (SLAM): part II

20062,503 citationsJournal Article

Authors

Hugh Durrant‐Whyte · University of Oxford

Abstract

This paper discusses the recursive Bayesian formulation of the simultaneous localization and mapping (SLAM) problem in which probability distributions or estimates of absolute or relative locations of landmarks and vehicle pose are obtained. The paper focuses on three key areas: computational complexity; data association; and environment representation.

Topics & Keywords

Robotics and Sensor-Based Localization Target Tracking and Data Fusion in Sensor Networks Automated Road and Building Extraction

UN Sustainable Development Goals

Sustainable cities and communities

Publication Details

Published in: IEEE Robotics & Automation Magazine

Volume 13, Issue 3, pp. 108-117

DOI: 10.1109/mra.2006.1678144

Field-Weighted Citation Impact: 1892.52