Robust 2D Indoor Localization Through Laser SLAM and Visual SLAM Fusion

Chan, Shao-Hung; Wu, Ping-Tsang; Fu, Li-Chen

doi:10.1109/smc.2018.00221

Cited by 82 publications

(40 citation statements)

References 22 publications

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“…From the experiment in corridor or hallway environment, we find that if the diameter of the scanning range of the laser sensor is longer than the length of hallway, human assisted positioning is effective; otherwise the robot can not get correct localization. This aspect of research is involved in [ 39 ].…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Visual Features Assisted Robot Localization in Symmetrical Environment Using Laser SLAM

Zhang

Jiang

et al. 2021

Sensors

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Localization for estimating the position and orientation of a robot in an asymmetrical environment has been solved by using various 2D laser rangefinder simultaneous localization and mapping (SLAM) approaches. Laser-based SLAM generates an occupancy grid map, then the most popular Monte Carlo Localization (MCL) method spreads particles on the map and calculates the position of the robot by a probabilistic algorithm. However, this can be difficult, especially in symmetrical environments, because landmarks or features may not be sufficient to determine the robot’s orientation. Sometimes the position is not unique if a robot does not stay at the geometric center. This paper presents a novel approach to solving the robot localization problem in a symmetrical environment using the visual features-assisted method. Laser range measurements are used to estimate the robot position, while visual features determine its orientation. Firstly, we convert laser range scans raw data into coordinate data and calculate the geometric center. Secondly, we calculate the new distance from the geometric center point to all end points and find the longest distances. Then, we compare those distances, fit lines, extract corner points, and calculate the distance between adjacent corner points to determine whether the environment is symmetrical. Finally, if the environment is symmetrical, visual features based on the ORB keypoint detector and descriptor will be added to the system to determine the orientation of the robot. The experimental results show that our approach can successfully determine the position of the robot in a symmetrical environment, while ordinary MCL and its extension localization method always fail.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Visual Features Assisted Robot Localization in Symmetrical Environment Using Laser SLAM

Zhang

Jiang

et al. 2021

Sensors

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“…Indoor mobile robotics with an onboard computational capacity and a better payload can easily incorporate a light two-dimensional (2D) laser rangefinder, which will be considered as an additional sensor that will greatly improve the results of the localization and mapping system under certain environment conditions. Previous studies on 2D indoor localization focus towards the laser SLAM and visual SLAM fusion to provide robust localization [11]. Hess et al also report the use of portable laser range finders for generating and visualizing floor plans in real-time [12].…”

Section: Related Work For Kidnapping Problem In Localizationmentioning

confidence: 99%

Efficient Detection of Robot Kidnapping in Range Finder-Based Indoor Localization Using Quasi-Standardized 2D Dynamic Time Warping

Ismail

Bukhori

2021

Applied Sciences

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This paper proposes an augmented online approach to detect kidnapping events within range-finder-based indoor localization. The method is specifically designed for an Internet of Things (IoT)-Aided Robotics Platform that enables the system to detect kidnapping across all time instances of an indoor mobile robotic operation with high accuracy and maintain a high accuracy in the face of relocalization failures. The approach is based on similarity degree of geometry shape of the environment obtained from range scan data between two consecutive time instances. The proposed approach named Quasi-Standardized Two-Dimensional Dynamic Time Warping (QS-2DDTW) is based on the Multidimensional Dynamic Time Warping (MD-DTW) with homogeneity variance test imbued in it. A series of simulations are preformed against maximum current weight, measurement entropy, and the four metrics in metric based detector. The result shows that the proposed method yields high performance in terms of its ability to distinguish kidnapping condition from normal condition and that it has low dependency towards relocalization process, thus ensures the accuracy of detection is not disturbed by relocalization.

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“…For that reason, it is the most reliable mechanism of data acquisition for the SLAM algorithm input [ 11 ]. Some approaches use a fusion of laser and visual data [ 41 ]. These algorithms, depending on a type, can operate on 2-dimensional [ 12 , 42 , 43 ] data or 3-dimensional [ 44 , 45 ] data (or both [ 21 ]).…”

Section: Introductionmentioning

confidence: 99%

LiDAR Point Cloud Generation for SLAM Algorithm Evaluation

Sobczak

Filus

Domański

et al. 2021

Sensors

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With the emerging interest in the autonomous driving level at 4 and 5 comes a necessity to provide accurate and versatile frameworks to evaluate the algorithms used in autonomous vehicles. There is a clear gap in the field of autonomous driving simulators. It covers testing and parameter tuning of a key component of autonomous driving systems, SLAM, frameworks targeting off-road and safety-critical environments. It also includes taking into consideration the non-idealistic nature of the real-life sensors, associated phenomena and measurement errors. We created a LiDAR simulator that delivers accurate 3D point clouds in real time. The point clouds are generated based on the sensor placement and the LiDAR type that can be set using configurable parameters. We evaluate our solution based on comparison of the results using an actual device, Velodyne VLP-16, on real-life tracks and the corresponding simulations. We measure the error values obtained using Google Cartographer SLAM algorithm and the distance between the simulated and real point clouds to verify their accuracy. The results show that our simulation (which incorporates measurement errors and the rolling shutter effect) produces data that can successfully imitate the real-life point clouds. Due to dedicated mechanisms, it is compatible with the Robotic Operating System (ROS) and can be used interchangeably with data from actual sensors, which enables easy testing, SLAM algorithm parameter tuning and deployment.

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Robust 2D Indoor Localization Through Laser SLAM and Visual SLAM Fusion

Cited by 82 publications

References 22 publications

Visual Features Assisted Robot Localization in Symmetrical Environment Using Laser SLAM

Visual Features Assisted Robot Localization in Symmetrical Environment Using Laser SLAM

Efficient Detection of Robot Kidnapping in Range Finder-Based Indoor Localization Using Quasi-Standardized 2D Dynamic Time Warping

LiDAR Point Cloud Generation for SLAM Algorithm Evaluation

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