Good quality of environment mapping demands modelling the associated environment nearly to its 3D originality. This paper presents a unified Simultaneous Localisation And Mapping (SLAM) solution based on partial 3D structure. As compared to existing representations such as grid based mapping, the novelty of the proposed unified approach lies in estimation, representation and handling of compact partial 3D features-based map model for a team of robots that are working in an unknown environment with unknown poses. The approach replies on a camera to perceive the environment and a 2D laser sensor to generate a SLAM solution with partial 3D features based representation. Extended Kalman Filter (EKF) estimates the robot pose based on its motion model and map of the explored environment. The solution has been tested in an indoor environment on two identical custom-developed robots. Experimental results have demonstrated efficacy of the approach. The presented solution can be easily applied on a distributed/centralized robotic system with ease of data handling and reduced computational cost.
- Grid mapping