An adaptive localization system for outdoor/indoor navigation for autonomous robots

2006 ◽  
Author(s):  
E. B. Pacis ◽  
B. Sights ◽  
G. Ahuja ◽  
G. Kogut ◽  
H. R. Everett
Keyword(s):  
Autonomous Robots ◽  
Indoor Navigation ◽  
Localization System

Empirical analysis of cyber-attacks to an indoor real time localization system for autonomous robots

2017 ◽  
Vol 70 ◽  
pp. 422-435 ◽  
Author(s):  
Ángel Manuel Guerrero-Higueras ◽  
Noemí DeCastro-García ◽  
Francisco Javier Rodríguez-Lera ◽  
Vicente Matellán
Keyword(s):  
Real Time ◽  
Empirical Analysis ◽  
Autonomous Robots ◽  
Cyber Attacks ◽  
Localization System

scholarly journals Fast Transfer Navigation for Autonomous Robots

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Chen Wang ◽  
Xudong Li ◽  
Xiaolin Tao ◽  
Kai Ling ◽  
Quhui Liu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Indoor Environment ◽  
Autonomous Robots ◽  
Indoor Navigation ◽  
Robotic Navigation ◽  
Navigation Algorithm ◽  
Proposed Model ◽  
Interior Environment

Navigation technology enables indoor robots to arrive at their destinations safely. Generally, the varieties of the interior environment contribute to the difficulty of robotic navigation and hurt their performance. This paper proposes a transfer navigation algorithm and improves its generalization by leveraging deep reinforcement learning and a self-attention module. To simulate the unfurnished indoor environment, we build the virtual indoor navigation (VIN) environment to compare our model and its competitors. In the VIN environment, our method outperforms other algorithms by adapting to an unseen indoor environment. The code of the proposed model and the virtual indoor navigation environment will be released.

scholarly journals Indoor Navigation Using Particle Filter and Sensor Fusion

2012 ◽  
Vol 19 (2) ◽  
pp. 31-40
Author(s):  
Lukas Köping ◽  
Thomas Mühsam ◽  
Christian Ofenberg ◽  
Bernhard Czech ◽  
Michael Bernard ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Particle Filter ◽  
Indoor Localization ◽  
High Accuracy ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Sensor Data ◽  
Localization System ◽  
Multiple Sensor ◽  
Large Building

Abstract In this paper we present an indoor localization system based on particle filter and multiple sensor data like acceleration, angular velocity and compass data. With this approach we tackle the problem of documentation on large building yards during the construction phase. Due to the circumstances of such an environment we cannot rely on any data from GPS, Wi-Fi or RFID. Moreover this work should serve us as a first step towards an all-in-one navigation system for mobile devices. Our experimental results show that we can achieve high accuracy in position estimation.

scholarly journals Drift-Aware Monocular Localization Based on a Pre-Constructed Dense 3D Map in Indoor Environments

2018 ◽  
Vol 7 (8) ◽  
pp. 299 ◽  
Author(s):  
Guanyuan Feng ◽  
Lin Ma ◽  
Xuezhi Tan ◽  
Danyang Qin
Keyword(s):  
Relative Position ◽  
Detection Method ◽  
Weighted Least Squares ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Feature Points ◽  
Line Model ◽  
Localization System ◽  
The Absolute ◽  
Global And Local

Recently, monocular localization has attracted increased attention due to its application to indoor navigation and augmented reality. In this paper, a drift-aware monocular localization system that performs global and local localization is presented based on a pre-constructed dense three-dimensional (3D) map. In global localization, a pixel-distance weighted least squares algorithm is investigated for calculating the absolute scale for the epipolar constraint. To reduce the accumulative errors that are caused by the relative position estimation, a map interaction-based drift detection method is introduced in local localization, and the drift distance is computed by the proposed line model-based maximum likelihood estimation sample consensus (MLESAC) algorithm. The line model contains a fitted line segment and some visual feature points, which are used to seek inliers of the estimated feature points for drift detection. Taking advantage of the drift detection method, the monocular localization system switches between the global and local localization modes, which effectively keeps the position errors within an expected range. The performance of the proposed monocular localization system is evaluated on typical indoor scenes, and experimental results show that compared with the existing localization methods, the accuracy improvement rates of the absolute position estimation and the relative position estimation are at least 30.09% and 65.59%, respectively.

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