Laser Based Localization Techniques for Indoor Mobile Robots

Tamás, Levente; Lazea, Gheorghe; Popa, M.; Szöke, István; Majdik, András

doi:10.1109/at-equal.2009.43

Cited by 10 publications

(8 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Underground Mine (Ratshidaho et al 2014;Ghosh et al 2017b;Artan et al 2012; Zlot and Bosse2013) 5 21 Simulated environment (Fauser et al 2017;Ma et al 2016; 3 13 Lab/Corridor Shamseldin et al 2018;Niu et al 2017b;Ramer et al 2015;Baatar et al 2014;Tang et al 2014;Hu et al 2013;Wang et al 2011;Tamas et al 2009;Xu et al 2019;Shi et al 2018;Wang et al 2014;Guanglei et al 2014;Lee and Park2013;Keller and Sternberg2013) 14 58…”

Section: Accuracy Validation Of the Positioning Methodsmentioning

confidence: 99%

“…Thus, lack of mapping system calibration can be considered one of the main common limitations in the papers analyzed. (Losch et al n.d.;Niu et al 2017b;Hu et al 2013;Wang et al 2011;Tamas et al 2009;Zarei and Ramezani2018;Shi et al 2018;Fauser et al 2017;Ghosh et al 2017b;Ma et al 2016;Wang et al 2014;Keller and Sternberg2013;Artan et al 2012;Zlot and Bosse2013) 18 75…”

Section: R a F Tmentioning

confidence: 99%

“…LiDAR (Losch et al 2019;Shamseldin et al 2018;Niu et al 2017b;Ramer et al 2015;Ratshidaho et al 2014;Hu et al 2013;Wang et al 2011;Tamas et al 2009;Xu et al 2019;Shi et al 2018;Ghosh et al 2017b;Ma et al 2016;Wang et al 2014;Guanglei et al 2014;Lee and Park2013;Keller and Sternberg2013;Artan et al 2012; Zlot and Bosse2013) 20 83 Odometer (Ramer et al 2015;Baatar et al 2014;Tang et al 2014;Hu et al 2013;Tamas et al 2009;Xu et al 2019;Zarei and Ramezani2018;Shi et al 2018;Fauser et al 2017;Ghosh et al 2017b;Ma et al 2016;Wang et al 2014;Guanglei et al 2014;Lee and Park2013;Keller and Sternberg2013;Artan et al 2012) 16 67 Camera (Losch et al 2019;Ramer et al 2015;Baatar et al 2014;Wang et al 2011;Fauser et al 2017;Lee and Park2013;Keller and Sternberg2013) 8 33…”

unclassified

See 2 more Smart Citations

Precise indoor localization and mapping using mobile laser scanners: a scoping review

et al. 2021

View full text Add to dashboard Cite

Indoor localization and mapping are essential for a wide range of applications. The absence of GPS signals in indoor environments such as buildings, caves, and tunnels brings significant challenges for applications where accurate positioning (i.e., centimeter-level accuracy) is required. This paper presents a scoping review of the most recent studies on precise indoor localization and mapping using mobile technologies, specifically, mobile laser scanners. The scoping review allows for a comprehensive and structured review of the literature to maximize the capture of relevant information and provide reproducible results. We extracted and reported a range of information from the selected articles published since 2009, with the goal of identifying the most frequently used sensors and methods of fusing their collected observations. The results show that in the majority of studies, LiDAR is the core sensor and IMUs with 75% and odometers with 67% magnitude are the main sensors integrated with the LiDAR system to enhance the localization precision. In addition, the classical iterative closest point (ICP) algorithm with approximately 60% frequency and the extended Kalman filter (EKF) method with over 40% frequency are the main algorithms used for the scan matching and fusion of different sensor data, respectively. This scoping review also revealed the lack of mapping-systems calibration as the main limitation in over 70% of the papers analyzed.

show abstract

Section: Accuracy Validation Of the Positioning Methodsmentioning

confidence: 99%

Section: R a F Tmentioning

confidence: 99%

unclassified

See 1 more Smart Citation

Precise indoor localization and mapping using mobile laser scanners: a scoping review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…On the left hand side the Bumblebee2 stereo camera and the LMS200 laser range finder (LRF) is shown. These sensors were used for the pedestrian recognition and they were mounted on a metal support [25]. On the right hand side of the figure the breaking system is presented.…”

Section: Active Car Breaking Systemsmentioning

confidence: 99%

Detection and tracking experiments in various environments

Tamás

Marcu

2011

2011 15th International Conference on Advanced Robotics (ICAR)

Self Cite

View full text Add to dashboard Cite

This paper presents the details of several mobile robot experiment designs including indoor, outdoor and urban variants. The aim of the paper is to give insights to setting up these tracking experiments covering both the software and hardware components as well as the application details. Beside these aspects the references to the perception, tracking and estimation parts are also pointed out for a wheel based mobile robots, bicycles and auto vehicles in various scenarios covering both indoor and outdoor variants.

show abstract

“…However, because of accumulated errors, localization accuracy exists during long-term operation will gradually decrease. Thanks to the high ranging accuracy, little influence of light, and easy installation, lidar is popular in various autonomous robots [15][16][17]. However, the effective measure distance is limited, and the matching-based method has the disadvantages of high cost and low efficiency in achieving global localization.…”

Section: Introductionmentioning

confidence: 99%

UAPF: A UWB Aided Particle Filter Localization For Scenarios with Few Features

Wang

Zhang

et al. 2020

Sensors

View full text Add to dashboard Cite

Lidar-based localization doesn’t have high accuracy in open scenarios with few features, and behaves poorly in robot kidnap recovery. To address this problem, an improved Particle Filter localization is proposed who could achieve robust robot kidnap detection and pose error compensation. UAPF adaptively updates the covariance by Jacobian from Ultra-wide Band information instead of predetermined parameters, and determines whether robot kidnap occurs by a novel criterion called KNP (Kidnap Probability). Besides, pose fusion of ranging-based localization and PF-based localization is conducted to decrease the uncertainty. To achieve more accurate ranging-based localization, linear regression of ranging data adopts values of maximum probability rather than average distances. Experiments show UAPF can achieve robot kidnap recovery in less than 2 s and position error is less than 0.1 m in a hall of 40 by 15 m, when the currently prevalent lidar-based localization costs more than 90 s and converges to wrong position.

show abstract

Laser Based Localization Techniques for Indoor Mobile Robots

Cited by 10 publications

References 4 publications

Precise indoor localization and mapping using mobile laser scanners: a scoping review

Precise indoor localization and mapping using mobile laser scanners: a scoping review

Detection and tracking experiments in various environments

UAPF: A UWB Aided Particle Filter Localization For Scenarios with Few Features

Contact Info

Product

Resources

About