Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion - Part B: Mapping and localisation

Accurate mobile robot localisation in orchards relies on precise orchard maps which help the mobile robot to efficiently estimate its position and orientation while moving between tree rows. This paper presents a new method for constructing a local orchard map based on tree trunk detection using camera and laser scanner data fusion. The final orchard map consists of the positions of the trees and non-tree objects (e.g. posts and tree supports) in the tree rows. The map of the individual trees is used as an a priori map to localise the mobile robot in the orchard. A data fusion algorithm based on an Extended Kalman Filter is used for position estimation. Experimental tests were conducted with a small robot platform in a real orchard environment to evaluate the performance of orchard mapping and mobile robot localisation. The mapping method successfully localised all the trees and non-tree objects of the tested tree rows in the orchard. The mapping results indicate that the constructed orchard map can be reliably used for mobile robot localisation and navigation. The localisation algorithm was evaluated against the logged RTK-GPS positions for different paths and headland turns. The average of the root mean square of the Euclidean distance between the ground truth and the estimated position for different paths was 0.103 m, whilst the average of the root mean square of the heading error was 3.32°.