A multi-feature localization algorithm for mobile robots indoor environment mapping

SUMMARY Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

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Path Planning in a dynamic indoor environment for mobile robots using Q-Learning Technique

2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) ◽

10.1109/miucc52538.2021.9447662 ◽

2021 ◽

Author(s):

Ahmed M. Walied ◽

Ahmed Onsy ◽

Shady A. Maged ◽

Sherif Hammad

Keyword(s):

Path Planning ◽

Mobile Robots ◽

Indoor Environment ◽

Q Learning ◽

Learning Technique

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Simulating actions and perception of autonomous mobile robots in a multi-agent indoor environment

Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91 ◽

10.1109/iros.1991.174719 ◽

2002 ◽

Cited By ~ 1

Author(s):

P. Lemoine ◽

C. Le Pape

Keyword(s):

Mobile Robots ◽

Indoor Environment ◽

Autonomous Mobile Robots ◽

Multi Agent

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A Localization Algorithm Research to Eliminate the Indoor NLOS Error

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.2232 ◽

2014 ◽

Vol 989-994 ◽

pp. 2232-2236 ◽

Cited By ~ 1

Author(s):

Jia Zhi Dong ◽

Yu Wen Wang ◽

Feng Wei ◽

Jiang Yu

Keyword(s):

Indoor Environment ◽

Arrival Time ◽

Channel Model ◽

Indoor Positioning ◽

Superior Performance ◽

Line Of Sight ◽

Localization Algorithm ◽

Positioning Accuracy ◽

Software Simulation ◽

Positioning Algorithm

Currently, there is an urgent need for indoor positioning technology. Considering the complexity of indoor environment, this paper proposes a new positioning algorithm (N-CHAN) via the analysis of the error of arrival time positioning (TOA) and the channels of S-V model. It overcomes an obvious shortcoming that the accuracy of traditional CHAN algorithm effected by no-line-of-sight (NLOS). Finally, though MATLAB software simulation, we prove that N-CHAN’s superior performance in NLOS in the S-V channel model, which has a positioning accuracy of centimeter-level and can effectively eliminate the influence of NLOS error on positioning accuracy. Moreover, the N-CHAN can effectively improve the positioning accuracy of the system, especially in the conditions of larger NLOS error.

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