Tobias Low

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

A novel tree trunk detection algorithm uses camera and laser scanner data fusion.Discriminates between trees and non-tree objects in the orchard.Automatic adjustment of colour parameters increased the detection accuracy.Evaluation under different illumination conditions (sunny, cloudy).Using a small robot platform, a detection accuracy of 96.64% was achieved in a real orchard. Trees in orchards are natural landmarks providing suitable cues for mobile robot localisation as they are nominally planted in straight and parallel rows. This paper presents a novel tree trunk detection algorithm using a camera and laser scanner data fusion to enhance the detection capability. The algorithm detects the trees in the orchard and discriminates between trees and non-tree objects (e.g. posts and tree supports). The laser scanner is used to detect the edge points and determine the width of the tree trunks and non-tree objects, while the camera images are used to verify the colour and the parallel edges of the tree trunks and non-tree objects. The algorithm automatically adjusts the colour detection parameters after each test which shown to increase the detection accuracy. Experimental tests were conducted with a small robot platform in a real orchard environment to evaluate the performance of the tree trunk detection algorithm under two broad illumination conditions (sunny and cloudy). The algorithm was able to detect the tree trunks and discriminate between trees and non-tree objects with detection accuracy of 96.64% showing that the fusion of both vision and laser scanner technologies produced robust tree trunk detection.

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°.

Ground-plane classification for robot navigation: Combining multiple cues toward a visual-based learning system

This paper describes a vision-based ground-plane classification system for autonomous indoor mobile-robot that takes advantage of the synergy in combining together multiple visual-cues. A priori knowledge of the environment is important in many biological systems, in parallel with their reactive systems. As such, a learning model approach is taken here for the classification of the ground/object space, initialised through a new Distributed-Fusion (D-Fusion) method that captures colour and textural data using Superpixels. A Markov Random Field (MRF) network is then used to classify, regularise, employ a priori constraints, and merge additional ground/object information provided by other visual cues (such as motion) to improve classification images. The developed system can classify indoor test-set ground-plane surfaces with an average true-positive to false-positive rate of 90.92% to 7.78% respectively on test-set data. The system has been designed in mind to fuse a variety of different visual-cues. Consequently it can be customised to fit different situations and/or sensory architectures accordingly.

An integrated WSN and mobile robot system for agriculture and environment applications

Agriculture and environment issues are becoming increasingly important and are facing some new challenges. It is believed that wireless Sensor Networks (WSNs) and machine automation are among the key enabling technologies to address these challenging issues. Although extensive research has been conducted on individual technologies, their seamless integration to solve complex environmental problems has not been done before. This paper provides a design concept and some preliminary results for an integrated autonomous monitoring system. The integrated system will provide a powerful and cost-efficient tool for optimal, profitable, and sustainable management of environment and agriculture and thus bring significant social and economic benefits.

Fabrication of a silica aerogel and examination of its hydrophobic properties via contact angle and 3M water repellency tests

Aerogels are dry gels with a very high specific pore volume. Aerogels with increased hydrophobicity have significant potential to expand their use as lightweight materials. Considering its special nanostructure and exceptional properties, this paper focuses on the synthesis and hydrophobic evaluation of a silica aerogel. The structural properties were investigated by measuring density, SEM micrographs, and BET analyses. Also, the hydrophobic evaluation was carried out by measuring 3M water repellency and water/alcohol contact angle. The BET analysis showed successful synthesis of the nanoporous silica aerogel with a pore size of 24 nm and porosity of 89%. The synthesized aerogel showed 3M water repellency of 3 and water contact angle of 129.6°. Also, it is worth-mentioning that as the alcohol content of the drops in 3M water repellency test is increased, the drop contact angle is decreased due to its lower surface tension. Thus, the contact angle reaches the zero at 3M water repellency test number of 4 (water/alcohol 60/40).