研究目的
To reduce the errors caused by wheel slipping in odometry-based SLAM and refine the map by combining an omnidirectional-wheeled based floor odometry sensor and a 360o lidar sensor under Iterative Closest Point (ICP) SLAM platform.
研究成果
ICP based SLAM can be used to build a map of a certain environment and reduce position and orientation errors from odometry sensor and IMU. A bigger number of iteration of ICP process results in better alignment but has a limit where adding more iterations no longer improves the map image.
研究不足
The ICP algorithm faces difficulties in map matching process in narrow spaces and sudden changes in the environment. The accuracy of the map is affected by the number of iterations in the ICP process.
1：Experimental Design and Method Selection:
The research combines an omnidirectional-wheeled based floor odometry sensor and a 360o lidar sensor under ICP SLAM platform to reduce errors caused by wheel slipping.
2：Sample Selection and Data Sources:
The experiment is conducted in a simulated environment using Vrep simulator and in a real-world laboratory room.
3：List of Experimental Equipment and Materials:
Hokuyo-URG-04LX LRF sensor, three omni directional wheels, rotary encoder sensor, inertial measurement unit (IMU).
4：Experimental Procedures and Operational Workflow:
The mobile robot explores the environment, captures data using LRF sensor, and uses ICP algorithm for map matching.
5：Data Analysis Methods:
The research analyzes the accumulated error values generated by the ICP algorithm with different iterations.
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