Simultaneous localization and mapping (SLAM): part II 论文

2006IEEE Robotics & Automation Magazine引用 2513
Robotics and Sensor-Based LocalizationTarget Tracking and Data Fusion in Sensor NetworksAutomated Road and Building Extraction

摘要

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.