An Indoor Mobile Robot Positioning Algorithm Based on Adaptive Federated Kalman Filter

Recently, most of the existing mobile robot indoor positioning systems (IPSs) use infrared sensors, cameras, and other extra infrastructures. They usually suffer from high cost and special hardware implementation. In order to address the above problems, this article proposes a Wi-Fi-based indoor mobile robot positioning system and designs and develops a robot positioning platform based on the commercial Wi-Fi devices, such as Wi-Fi routers. Furthermore, a robust principal component analysis-based extreme learning machine algorithm is proposed to address the issue of noisy measurements in IPSs. Real-world robot indoor positioning experiments are extensively carried out and the results verify the effectiveness and superiority of the proposed system.

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A SLAM algorithm for indoor mobile robot localization using an Extended Kalman filter and a segment based environment mapping

2013 16th International Conference on Advanced Robotics (ICAR) ◽

10.1109/icar.2013.6766461 ◽

2013 ◽

Cited By ~ 8

Author(s):

Luigi D'Alfonso ◽

Andrea Griffo ◽

Pietro Muraca ◽

Paolo Pugliese

Keyword(s):

Kalman Filter ◽

Mobile Robot ◽

Extended Kalman Filter ◽

Robot Localization ◽

Indoor Mobile Robot ◽

Mobile Robot Localization ◽

Slam Algorithm

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Design and Robust Recognition of Omnidirectional Landmarks for Indoor Mobile Robot Positioning

Volume 4: 18th Design for Manufacturing and the Life Cycle Conference; 2013 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications ◽

10.1115/detc2013-12256 ◽

2013 ◽

Author(s):

Hanyu Liu ◽

Xu Zhong ◽

Yu Zhou

Keyword(s):

Mobile Robot ◽

Recognition Algorithm ◽

Positioning System ◽

Contour Analysis ◽

Landmark Recognition ◽

Black And White ◽

Indoor Mobile Robot ◽

Robot Positioning ◽

Robust Recognition ◽

Landmark Model

In this paper, we present an omnidirectional artificial landmark model and a robust artificial landmark recognition algorithm for indoor mobile robot positioning. The landmark model encodes identities with nested circles in black and white, which provides stable edge response and enables strong tolerance to various lighting conditions and perspective distortions. The corresponding positioning system uses a single upward-facing webcam as the vision sensor to capture landmarks. To address the effect of the lighting and sensing noise, the topological contour analysis is applied to detect landmarks, and the dynamic illumination adjustment is used to assist landmark recognition. Based on the landmark recognition, the absolute position of the camera in the environment is estimated using a trilateration algorithm. The landmark model and positioning system are tested with a mobile robot in a real indoor environment. The results show that the purposed technique provides autonomous indoor positioning for mobile robots with high robustness and consistency.

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BUILDING MAP AND POSITIONING SYSTEM FOR INDOOR ROBOT BASED ON PLAYER

International Journal of Information Acquisition ◽

10.1142/s0219878911002483 ◽

2011 ◽

Vol 08 (04) ◽

pp. 281-290

Author(s):

BIN WANG ◽

WEI LU ◽

BIN KONG

Keyword(s):

Mobile Robot ◽

Positioning System ◽

Map Building ◽

Adaptive Monte Carlo ◽

Indoor Mobile Robot ◽

Robot Positioning ◽

Positioning Method ◽

Dynamic Map ◽

Slam Algorithm ◽

Number Of Particles

In this paper, we have proposed a map-building and positioning method for an indoor mobile robot based on the open source platform Player. First, the DP-SLAM algorithm is transplanted to the Player and used to build the dynamic offline map. This would reduce the errors and constraints caused by manual map building. Second, the KLD-Sampling Adaptive Monte Carlo Locating (KLD-AMCL) algorithm is introduced to reduce the number of particles required in locating. Meanwhile, higher accuracy of localization is achieved through calculating the MLE and the real posterior KL distance. Finally, an indoor mobile robot positioning system is built by combining the Player platform, dynamic map building and KLD-AMCL algorithm. Experimental results show that the proposed system has better environmental adaptability and higher positioning accuracy.

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Design of Intelligent Mobile Robot Positioning Algorithm Based on IMU/Odometer/Lidar

2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) ◽

10.1109/sdpc.2019.00118 ◽

2019 ◽

Author(s):

Zhaodong Li ◽

Zhibao Su ◽

Tingting Yang

Keyword(s):

Mobile Robot ◽

Robot Positioning ◽

Positioning Algorithm

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An improved measurement variable estimation model for positioning mobile robot

Interaction Studies ◽

10.1075/is.18014.qu ◽

2019 ◽

Vol 20 (1) ◽

pp. 78-101 ◽

Cited By ~ 1

Author(s):

Junsuo Qu ◽

Leichao Hou ◽

Ruijun Zhang ◽

Zhiwei Zhang ◽

Qipeng Zhang ◽

...

Keyword(s):

Kalman Filter ◽

Mobile Robot ◽

Manufacturing Industry ◽

Unscented Kalman Filter ◽

Measurement Model ◽

Smart Manufacturing ◽

Key Factors ◽

Estimation Model ◽

Robot Positioning ◽

Time Update

Abstract The localization and navigation technology are the key factors in the research of mobile robots. With the demand of smart manufacturing industry and the development of robotics technology, the importance of mobile robot has become increasingly prominent. Mobile robot positioning research is mostly based on odometry, however, it has cumulative errors that would affect the accuracy of positioning results. This paper describes an improved measurement model that suitable from 0° to 180° and used this model in the Extended Kalman Filter (EKF) and Unscented Kalman Filter(UKF) time update step respectively, the method can address the interference of kinematics model predicted position and heading angle, both of them are easily disturbed by noises and other factors. Designing a tracked mobile robot as experimental platform to collect the raw data, conducting experimental research including the performance of hardware platform and autonomous obstacle avoidance, the real-time and stability of remote data interaction, and the accuracy of optimal pose estimation. The simulation results have been verified the accuracy of the improved measurement model applied to UKF.

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