Navigation of a Mobile Agribot by using Multi-sensor “SLAM”

Abstract

Applications of mobile robots are continuously capturing importance in numerous areas such as agriculture, surveillance, defense and planetary exploration to name a few. Accurate navigation of a mobile robot is highly significant for its uninterrupted operation. Simultaneous localization and mapping (SLAM) is one of the widely used techniques in mobile robots for localization and navigation. SLAM consists of front and back end processes, wherein, the front end includes SLAM sensors. These sensors play signification role in acquiring accurate environmental information for further processing and mapping. Therefore, understanding the operational limits of the available SLAM sensors and accurate data collection techniques from single or multi-sensors is noteworthy. In this work, we optimize selection of SLAM sensors, and implemented multisensory SLAM. The performance of SLAM sensors is compared using the analytical hierarchy process (AHP) based on various key indicators such as accuracy, range, cost, working environment and computational cost. Simulation were performed gazebo environment using ROS for simultaneous localization and mapping (SLAM) with the key focus on navigation of the agribot in the indoor agricultural field. The SLAM was performed by fusion of data from multiple sensors. Obstacle avoidance and handling of computational cost was performed by using the sonar sensor. Localization of the landmarks was solved with using 2D LiDAR and Microsoft Kinect (RGBD) sensor without prior knowledge of the environment. A well-known SLAM technique (Extended Kalman Filter) was used for solving localization issues and building the map for the environment. Extended Kalman filter (EKF) based SLAM was implemented on a two-wheeled mobile robot with encoders (for localization of robot). The robot was programmed to autonomously navigate inside the indoor static environment. Sonar sensor was used for minimizing the time duration and computational cost during obstacle avoidance. In experiments, localization of landmarks and mapping are achieved with sonar sensor and LiDAR using EKF. The accuracy mapping were 93% and 97% during experimentation and simulation, respectively (with LiDAR). In RGBD-SLAM, accuracy of localization and mapping was 95% and 80%, respectively (from experiment). The accuracy of localization and mapping was 98% and 85% in RGBD SLAM with multi-sensors SLAM which include LiDAR, Microsoft Kinect, sonar and odometry sensor (in Gazebo simulation).