Abstract
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. DOI: http://dx.doi.org/10.5755/j01.eee.20.9.8707
Highlights
Simultaneous Localization and Mapping (SLAM) is considered as the fundamental problem in mobile robotics [1]. Both the map and the mobile robot position are not known and the robot moves through the environment, perceives it using on-board sensors and generates a map model
Due to sensor noises and process noises associated with motion of the robot, various probabilistic estimation techniques are used to explicitly model different sources of noises and their impact on measurements for efficient robotic map building
SLAM solutions have been successfully integrated with path planning and navigation issues to enhance autonomy of the moving robot
Summary
Simultaneous Localization and Mapping (SLAM) is considered as the fundamental problem in mobile robotics [1] Both the map and the mobile robot position are not known and the robot moves through the environment, perceives it using on-board sensors and generates a map model. Many researchers have proposed distinct solutions starting from late 80’s when stochastic techniques have started to replace deterministic based approaches in solving localization problem because of associated uncertainties in moving robots [3]. In [5], EKF based SLAM solution has been presented using features for map modeling while SEIF based solution has been proposed in [6]. Feature based EKF SLAM algorithm is proposed to solve localization and mapping issues.
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