Wireless Interconnected Mobile Robot Network Based Mapping and Scanning (PhD Thesis)
- Karachi, : NED University of Engineering and Technology Department of Electronic Engineering, 2022
- 145 p. : ill.
Includes Bibliographical References
Abstract
In recent years, researchers developed several solutions for simultaneous localization and mapping (SLAM). However, 3D map development and scanning of a large area by multi-robots are facing several challenges, like the development of 3D maps using integrated multiple scanners and the determination of the robot's relative possess for map merging. A solution of 3D environment scanning and mapping is presented using small, wheeled robotic units (rovers). It is a two-phase solution, in the first phase, a team of rovers scans the environment using an innovative orthogonal arrangement of two low-cost laser scanners, attached to each rover. One scanner scans the environment horizontally in XY plane and the other one scans vertically in YZ plane. The scans of the vertical scanner transforms with respect to the horizontal scanner. The combination of all scanning data develops a 3D point cloud map of the surveyed vicinity explored by the respective rover. In the second phase, all the maps developed by each rover merged using range data and a larger 3D point cloud map of the surveyed region has developed. The solution was validated through several experiments, performed in a real indoor environment. Each rover was deployed at different locations in the common environment. During the scanning, a 2D map was developed using the simultaneous localization and mapping (SLAM) technique. Hector SLAM package was applied to develop a 2D map on each rover. The operation of SLAM and abstraction of data of other sensors were performed in Robot Operating System (ROS) and the data has been utilized in offline mode. The generated 2D map data has been transformed in offline mode to develop a 3D map of each rover and later was further processed for merging with other independent maps. The merging was processed through sensors fusion application using Kalman Filter (KF) technique. The established mapping results of the system have been found 98% accurate if compared with the physical dimensions of the explored regions. The point-cloud data of the merged map were applied to the building information model (BIM) and found valid for the surveyed environment. In analogy to existing surveying and scanning solutions of the regional market, the presented solution found precise and convenient to provide structural information of surveyed entities at a highly affordable cost.
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SLAM Thesis Mapping Thesis Wheeled Robotic Units (Rovers) Thesis Kalman Filter KF Thesis