Simultaneous Global Localization and Mapping

Choi, Hyang Hee; Yang, Kwang Woong; Kim, Euntai

doi:10.1109/tmech.2013.2274822

Cited by 27 publications

(6 citation statements)

References 18 publications

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“…Encoders were used to calculate the linear displacement of each wheel. The new orientation of the robot could be estimated from the difference in the encoder counts, the diameter of the wheels, and distance between the wheels [1][2][3][4][5][8][9][10][11][12][13]. The encoder values were transferred to the remote station system through the wireless data transmission module to plot a 2D map in the Virtual Simulation software.…”

Section: Differential Drive Wheelmentioning

confidence: 99%

“…This will be helpful to find the radius of the arc 𝑅 L , 𝑅 C , and 𝑅 r and thereby the new position of the robot (𝑋 new , 𝑌 new , Ө new ) will be calculated. The latest position values are replaced with the previous position values and the software algorithm performs the calculation and updates the current path of the robot [9].…”

Section: Navigation Systemmentioning

confidence: 99%

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Efficient Computation for Localization and Navigation System for a Differential Drive Mobile Robot in Indoor and Outdoor Environments

Karupusamy¹,

Maruthachalam²,

Mayilswamy³

et al. 2021

RIA

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Numerous challenges are usually faced during the design and development of an autonomous mobile robot. Path planning and navigation are two significant areas in the control of autonomous mobile robots. The computation of odometry plays a major role in developing navigation systems. This research aims to develop an effective method for the computation of odometry using low-cost sensors, in the differential drive mobile robot. The controller acquires the localization of the robot and guides the path to reach the required target position using the calculated odometry and its created new two-dimensional mapping. The proposed method enables the determination of the global position of the robot through odometry calibration within the indoor and outdoor environment using Graphical Simulation software.

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Section: Differential Drive Wheelmentioning

confidence: 99%

Section: Navigation Systemmentioning

confidence: 99%

Efficient Computation for Localization and Navigation System for a Differential Drive Mobile Robot in Indoor and Outdoor Environments

Karupusamy¹,

Maruthachalam²,

Mayilswamy³

et al. 2021

RIA

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show abstract

“…To evaluate the performance of the proposed global localization method, the following four metrics were used as defined in [1]: the correct localization rate (CLR), false localization rate (FLR), localization failure rate (LFR), and first correct localization time (FCLT). The CLR is the ratio of the number of time steps of successful robot localization over the total number of time steps.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…Global localization is a fundamental problem in autonomous navigation of mobile robots [1], [2]. As commercial robots run into various challenging situations such as an unknown initial pose or the kidnapped robot problem, fast and accurate yet reliable global localization is highly required.…”

Section: Introductionmentioning

confidence: 99%

New Monte Carlo Localization Using Deep Initialization: A Three-Dimensional LiDAR and a Camera Fusion Approach

Kim

2020

IEEE Access

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Fast and accurate global localization of autonomous ground vehicles is often required in indoor environments and GPS-shaded areas. Typically, with regard to global localization problem, the entire environment should be observed for a long time to converge. To overcome this limitation, a new initialization method called deep initialization is proposed and it is applied to Monte Carlo localization (MCL). The proposed method is based on the combination of a three-dimensional (3D) light detection and ranging (LiDAR) and a camera. Using a camera, pose regression based on a deep convolutional neural network (CNN) is conducted to initialize particles of MCL. Particles are sampled from the tangent space to a manifold structure of the group of rigid motion. Using a 3D LiDAR as a sensor, a particle filter is applied to estimate the sensor pose. Furthermore, we propose a re-localization method for performing initialization whenever a localization failure or the situation of robot kidnapping is detected. Either the localization failure or the kidnapping is detected by combining the outputs from a camera and 3D LiDAR. Finally, the proposed method is applied to a mobile robot platform to prove the method's effectiveness in terms of both the localization accuracy and time consumed for estimating the pose correctly.

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“…SLAM makes use of hardware sensors' (e.g., camera, laser) measurement data to establish the environmental mapping schemes for estimating the position of the mobile robot [2]. SLAM has an important research significance in robotic control, navigation and mission planning [3].…”

Section: Introductionmentioning

confidence: 99%

Sparse Direct Robot Localization Method Based on RGB-D Camera

Rongbo¹,

Wu²,

Yeboah³

et al. 2018

Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)

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Abstract-In order to address the current challenges associated with feature-based RGB-D SLAM, this paper puts forward a novel sparse direct localization algorithm. Contributions of the paper are manifold. Firstly, the proposed algorithm achieves rapid feature-point detection as well as camera pose estimation through a minimization strategy of the photometric error associated with image coupling. Secondly, a computational optimization scheme is put forward for the proposed algorithm such that key-frames are selected adaptively using a spatial domain framework which monitors the robot's motion in real-time, and applies a Nearest Neighbor algorithm towards loop closure detection. Finally, the proposed algorithm achieves robot pose estimation and optimization in real-time using a General Framework for Graph Optimization (g2o) strategy. The performance of the proposed algorithm is verified through live robotic experimental evaluation. The achieved results suggest that the scheme attains significantly high localization accuracies with low RMSE within a range of 25 meters. For scenarios where the camera remains fixed, RMSE reaches up to 1% within a range of 29.6 meters. Furthermore, the proposed scheme achieves localization speeds of up to 45 fps, demonstrating superior real-time capabilities, and addressing computational drawbacks associated with state-of-the-art.Keywords-simultaneous localization and mapping; sparse direct method; keyframe selection; kinect I INTRODUCTIONOver the past decade, outdoor positioning has been rapidly developed and widely utilized based on satellite technologies, such as the Global Positioning System (GPS). However, we are more than 70% of the time indoors, therefore, indoor positioning technology offers great research and application value. Indoor environments are complex in nature, and it is therefore necessary to estimate the real-time position of mobile nodes from a series of measurement data. To the best of our knowledge, there are no working solutions with sufficient application potential thus far [1].In the field of robot localization, the Simultaneous Localization and Mapping (SLAM) algorithm, based on laser and vision has been widely relied upon. SLAM makes use of hardware sensors' (e.g., camera, laser) measurement data to establish the environmental mapping schemes for estimating the position of the mobile robot [2]. SLAM has an important research significance in robotic control, navigation and mission planning [3]. On the other hand, sensors are an important part of SLAM. Currently, sensors commonly used include laser radar, monocular/stereo/panoramic cameras, RGB-D cameras, Inertial Measurement Units and multiple sensor fusion schemes.Amongst them, RGB-D cameras (e.g., Kinect), which can simultaneously obtain the pixel colors and depth information, have been widely researched in recent years. Although the RGB-D SLAM system has evolved rapidly, it is still faced with some challenging issues such as a small Field of View (FOV) associated with the RGB-D camera as well as high comp...

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Simultaneous Global Localization and Mapping

Cited by 27 publications

References 18 publications

Efficient Computation for Localization and Navigation System for a Differential Drive Mobile Robot in Indoor and Outdoor Environments

Efficient Computation for Localization and Navigation System for a Differential Drive Mobile Robot in Indoor and Outdoor Environments

New Monte Carlo Localization Using Deep Initialization: A Three-Dimensional LiDAR and a Camera Fusion Approach

Sparse Direct Robot Localization Method Based on RGB-D Camera

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