P‐17.4: Simultaneous Localization and Mapping Method and Implementation for AGV

2021 ◽  
Vol 52 (S2) ◽  
pp. 1135-1137
Author(s):  
Yameng Zhu ◽  
Xuekai Yang ◽  
Wanlin Li
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Mapping Method ◽  
Localization And Mapping

scholarly journals Pedestrian Navigation System with Trinal-IMUs for Drastic Motions

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5570
Author(s):  
Yiming Ding ◽  
Zhi Xiong ◽  
Wanling Li ◽  
Zhiguo Cao ◽  
Zhengchun Wang
Keyword(s):  
Navigation System ◽  
Inertial Sensor ◽  
Estimation Error ◽  
Simultaneous Localization And Mapping ◽  
Step Length ◽  
Mapping Method ◽  
Zero Crossing ◽  
Pedestrian Navigation ◽  
Length Estimation ◽  
Localization And Mapping

The combination of biomechanics and inertial pedestrian navigation research provides a very promising approach for pedestrian positioning in environments where Global Positioning System (GPS) signal is unavailable. However, in practical applications such as fire rescue and indoor security, the inertial sensor-based pedestrian navigation system is facing various challenges, especially the step length estimation errors and heading drift in running and sprint. In this paper, a trinal-node, including two thigh-worn inertial measurement units (IMU) and one waist-worn IMU, based simultaneous localization and occupation grid mapping method is proposed. Specifically, the gait detection and segmentation are realized by the zero-crossing detection of the difference of thighs pitch angle. A piecewise function between the step length and the probability distribution of waist horizontal acceleration is established to achieve accurate step length estimation both in regular walking and drastic motions. In addition, the simultaneous localization and mapping method based on occupancy grids, which involves the historic trajectory to improve the pedestrian’s pose estimation is introduced. The experiments show that the proposed trinal-node pedestrian inertial odometer can identify and segment each gait cycle in the walking, running, and sprint. The average step length estimation error is no more than 3.58% of the total travel distance in the motion speed from 1.23 m/s to 3.92 m/s. In combination with the proposed simultaneous localization and mapping method based on the occupancy grid, the localization error is less than 5 m in a single-story building of 2643.2 m2.

scholarly journals Simultaneous Localization and Mapping Method Based on Improved Cubature Kalman Filter

2021 ◽  
Vol 33 (8) ◽  
pp. 2591
Author(s):  
Chaoyang Chen ◽  
Qi He ◽  
Qiubo Ye ◽  
Guangsong Yang ◽  
Cheng-Fu Yang
Keyword(s):  
Kalman Filter ◽  
Simultaneous Localization And Mapping ◽  
Mapping Method ◽  
Localization And Mapping ◽  
Cubature Kalman Filter

scholarly journals An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Jianjun Ni ◽  
Tao Gong ◽  
Yafei Gu ◽  
Jinxiu Zhu ◽  
Xinnan Fan
Keyword(s):  
Semantic Information ◽  
Hot Spot ◽  
Simultaneous Localization And Mapping ◽  
Mapping Method ◽  
Monocular Vision ◽  
Visual Slam ◽  
Camera Tracking ◽  
Residual Network ◽  
Semantic Map ◽  
Localization And Mapping

The robot simultaneous localization and mapping (SLAM) is a very important and useful technology in the robotic field. However, the environmental map constructed by the traditional visual SLAM method contains little semantic information, which cannot satisfy the needs of complex applications. The semantic map can deal with this problem efficiently, which has become a research hot spot. This paper proposed an improved deep residual network- (ResNet-) based semantic SLAM method for monocular vision robots. In the proposed approach, an improved image matching algorithm based on feature points is presented, to enhance the anti-interference ability of the algorithm. Then, the robust feature point extraction method is adopted in the front-end module of the SLAM system, which can effectively reduce the probability of camera tracking loss. In addition, the improved key frame insertion method is introduced in the visual SLAM system to enhance the stability of the system during the turning and moving of the robot. Furthermore, an improved ResNet model is proposed to extract the semantic information of the environment to complete the construction of the semantic map of the environment. Finally, various experiments are conducted and the results show that the proposed method is effective.

A Simultaneous Localization and Mapping Method Based on Fast-Hough Transform

2007 ◽  
Vol 7 (1) ◽  
pp. 190-194 ◽  
Author(s):  
Wu Zu Yu ◽  
Huang Xin Han ◽  
Li Xin de ◽  
Wang Min ◽  
Yan Huai Cheng
Keyword(s):  
Hough Transform ◽  
Simultaneous Localization And Mapping ◽  
Mapping Method ◽  
Localization And Mapping

scholarly journals TOWARDS GLOBALLY CONSISTENT SCAN MATCHINGWITH GROUND TRUTH INTEGRATION

2015 ◽  
Vol XL-3/W2 ◽  
pp. 59-64
Author(s):  
J. Gailis ◽  
A. Nüchter
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Simultaneous Localization And Mapping ◽  
Ground Truth ◽  
Mapping Method ◽  
Coordinate Frame ◽  
Scan Matching ◽  
Localization And Mapping ◽  
Real World Datasets ◽  
Ground Truth Information

The scan matching based simultaneous localization and mapping method with six dimensional poses is capable of creating a three dimensional point cloud map of the environment, as well as estimating the six dimensional path that the vehicle has travelled. The essence of it is the registering and matching of sequentially acquired 3D laser scans, while moving along a path, in a common coordinate frame in order to provide 6D pose estimations at the respective positions, as well as create a three dimensional map of the environment. An approach that could drastically improve the reliability of acquired data is to integrate available ground truth information. This paper is about implementing such functionality as a contribution to 6D SLAM (simultaneous localization and mapping with 6 DoF) in the 3DTK – The 3D Toolkit software (Nüchter and Lingemann, 2011), as well as test the functionality of the implementation using real world datasets.

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