Abstract
This paper describes our work in developing a 3D robotic mapping system composed by an experimental mobile platform equipped with a rotating laser range finder (LRF). For the purpose of obtaining more complete 3D scans of the environment, we design, construct and calibrate a crank-rocker four-bar linkage so that a LRF mounted on it could undergo repetitive rotational motion between two extreme positions, allowing both horizontal and vertical scans. To reduce the complexity of map representation suitable for optimization later, the local map from the LRF is a grid map represented by a distance-transformed (DT) matrix. We compare the DT-transformed maps and find the transformation matrix of a robot pose by a linear simplex-based map optimization method restricted to a local region allows efficient alignment of maps in scan matching. Several indoor 2D and 3D mapping experiments are presented to demonstrate the consistency, efficiency and accuracy of the 3D mapping system for a mobile robot that is stationary or in motion.
Highlights
Building an environment map is a popular topic in mobile robotic research and applications
We described the implementation and testing of a 3D robotic mapping system based on a scan matching method called simplex‐based Global Distance Transform (GDT) SLAM
The range measurements during the 3D map building process could be facilitated by a rotational mechanism design of a crank‐rocker four‐ bar linkage mounted on a mobile robot
Summary
Building an environment map is a popular topic in mobile robotic research and applications. The method of how to get information on the environment and robot localization is called SLAM (Simultaneous Localization and Mapping) [1,2,3,4,5,6,7,8,9,10]. With uncertainties in the environment and sensor readings, SLAM is one of the most fundamental methods for enhancing the robustness and efficiency of mobile robot navigation [5, 8], path planning, pursuing and patrolling. There are two major methods for constructing a map precisely and efficiently. One is a probabilistic method [28] and the other is scan matching. EKF (Extended Kalman Filter) [8, 9] and RBPF
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