2C11 Autonomous control and Simultaneous Localization and Mapping (SLAM) of Unmanned Ground Vehicle

Among the huge number of functionalities that are required for autonomous navigation, the most important are localization, mapping, and path planning. In this article, investigation of the path planning problem of unmanned ground vehicle is based on optimal control theory and simultaneous localization and mapping. A new approach of optimal simultaneous localization, mapping, and path planning is proposed. Our approach is mainly affected by vehicle’s kinematics and environment constraints. Simultaneous localization, mapping, and path planning algorithm requires two main stages. First, the simultaneous localization and mapping algorithm depends on the robust smooth variable structure filter estimate accurate positions of the unmanned ground vehicle. Then, an optimal path is planned using the aforementioned positions. The aim of the simultaneous localization, mapping, and path planning algorithm is to find an optimal path planning using the Shooting and Bellman methods which minimizes the final time of the unmanned ground vehicle path tracking. The simultaneous localization, mapping, and path planning algorithm has been approved in simulation, experiments, and including real data employing the mobile robot Pioneer [Formula: see text]. The obtained results using smooth variable structure filter–simultaneous localization and mapping positions and the Bellman approach show path generation improvements in terms of accuracy, smoothness, and continuity compared to extended Kalman filter–simultaneous localization and mapping positions.

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An Imaging Network Design for UGV-Based 3D Reconstruction of Buildings

Remote Sensing ◽

10.3390/rs13101923 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1923

Author(s):

Ali Hosseininaveh ◽

Fabio Remondino

Keyword(s):

3D Reconstruction ◽

Network Design ◽

Real World ◽

Continuous Image ◽

Unmanned Ground Vehicle ◽

Ground Vehicle ◽

Localization And Mapping ◽

Image Capturing ◽

Slam Algorithm ◽

Selection Of

Imaging network design is a crucial step in most image-based 3D reconstruction applications based on Structure from Motion (SfM) and multi-view stereo (MVS) methods. This paper proposes a novel photogrammetric algorithm for imaging network design for building 3D reconstruction purposes. The proposed methodology consists of two main steps: (i) the generation of candidate viewpoints and (ii) the clustering and selection of vantage viewpoints. The first step includes the identification of initial candidate viewpoints, selecting the candidate viewpoints in the optimum range, and defining viewpoint direction stages. In the second step, four challenging approaches—named façade pointing, centre pointing, hybrid, and both centre & façade pointing—are proposed. The entire methodology is implemented and evaluated in both simulation and real-world experiments. In the simulation experiment, a building and its environment are computer-generated in the ROS (Robot Operating System) Gazebo environment and a map is created by using a simulated robot and Gmapping algorithm based on a Simultaneously Localization and Mapping (SLAM) algorithm using a simulated Unmanned Ground Vehicle (UGV). In the real-world experiment, the proposed methodology is evaluated for all four approaches for a real building with two common approaches, called continuous image capturing and continuous image capturing & clustering and selection approaches. The results of both evaluations reveal that the fusion of centre & façade pointing approach is more efficient than all other approaches in terms of both accuracy and completeness criteria.

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Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas

2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422) ◽

10.1109/robot.2003.1241698 ◽

2004 ◽

Cited By ~ 28

Author(s):

Chieh-Chih Wang ◽

C. Thorpe ◽

S. Thrun

Keyword(s):

Urban Areas ◽

Moving Objects ◽

Simultaneous Localization And Mapping ◽

Ground Vehicle ◽

Detection And Tracking ◽

Localization And Mapping

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Autonomous navigation and mapping in a simulated environment

10.21079/11681/42006 ◽

2021 ◽

Author(s):

Benjamin Christie ◽

Osama Ennasr ◽

Garry Glaspell

Keyword(s):

Operating System ◽

Autonomous Navigation ◽

Three Dimensional ◽

Unmanned Ground Vehicle ◽

Ground Vehicle ◽

Robot Operating System ◽

Simulated Environment ◽

Localization And Mapping ◽

Environment Exploration ◽

Rgbd Cameras

Unknown Environment Exploration (UEE) with an Unmanned Ground Vehicle (UGV) is extremely challenging. This report investigates a frontier exploration approach, in simulation, that leverages Simultaneous Localization And Mapping (SLAM) to efficiently explore unknown areas by finding navigable routes. The solution utilizes a diverse sensor payload that includes wheel encoders, three-dimensional (3-D) LIDAR, and Red, Green, Blue and Depth (RGBD) cameras. The main goal of this effort is to leverage frontier-based exploration with a UGV to produce a 3-D map (up to 10 cm resolution). The solution provided leverages the Robot Operating System (ROS).

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