ORCID Profile
0000-0002-9542-9525
Current Organisation
University of South Australia
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Publisher: IEEE
Date: 12-2012
Publisher: Elsevier BV
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 04-07-2023
DOI: 10.1007/S10514-023-10110-Y
Abstract: In this research, we proposed a stereo visual simultaneous localisation and mapping (SLAM) system that efficiently works in agricultural scenarios without compromising the performance and accuracy in contrast to the other state-of-the-art methods. The proposed system is equipped with an image enhancement technique for the ORB point and LSD line features recovery, which enables it to work in broader scenarios and gives extensive spatial information from the low-light and hazy agricultural environment. Firstly, the method has been tested on the standard dataset, i.e., KITTI and EuRoC, to validate the localisation accuracy by comparing it with the other state-of-the-art methods, namely VINS-SLAM, PL-SLAM, and ORB-SLAM2. The experimental results evidence that the proposed method obtains superior localisation and mapping accuracy than the other visual SLAM methods. Secondly, the proposed method is tested on the ROSARIO dataset, our low-light agricultural dataset, and O-HAZE dataset to validate the performance in agricultural environments. In such cases, while other methods fail to operate in such complex agricultural environments, our method successfully operates with high localisation and mapping accuracy.
Publisher: IEEE
Date: 12-2013
Publisher: Wiley
Date: 05-12-2018
DOI: 10.1002/ASJC.1691
Publisher: Springer Science and Business Media LLC
Date: 04-2016
Publisher: Wiley
Date: 21-12-2021
DOI: 10.1002/ROB.22055
Abstract: Mobile robots need to automatically generate a safe, goal‐oriented, and fast collision‐free trajectory in real‐time during the movement in an indoor/outdoor environment. A planned trajectory must be adaptable and drivable with environmental changes where various static and moving obstacles may be present. The ultimate goal of a robot is to reach the destination without hitting any obstacles, therefore, a reactive local path planning algorithm is needed. In this paper, a novel local algorithm is proposed by integrating dynamic window approach (DWA) and improved follow the gap method (IFGM) to generate a collision‐free trajectory for a mobile robot which is capable to avoid any moving obstacles presenting in the surrounding environment. In this proposed method, first, a safety distance is maintained according to the relative position of obstacles and the robot. Moreover, find a feasible gap to direct the robot toward the desired goal. Besides, the heading angle is calculated to change the direction of the robot for avoiding collision with nearby obstacles. After that, calculate the appropriate velocity for the robot. Finally, a robust, safe, and goal‐directed trajectory is generated which does not suffer from global convergence and local minima problems. The performance and effectiveness of this proposed algorithm are evaluated by experimental results.
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 08-2017
Publisher: Elsevier BV
Date: 06-2019
Publisher: IEEE
Date: 09-2015
Publisher: IEEE
Date: 06-2013
Publisher: Public Library of Science (PLoS)
Date: 05-10-2023
Publisher: IEEE
Date: 06-2013
Publisher: Wiley
Date: 23-07-2015
DOI: 10.1002/ASJC.924
Publisher: American Chemical Society (ACS)
Date: 10-03-2023
Publisher: IEEE
Date: 06-2013
Publisher: Elsevier BV
Date: 07-2017
Publisher: MDPI AG
Date: 26-05-2022
Abstract: In this paper, a longitudinal and lateral control system of an autonomous vehicle is presented by developing a novel hybrid trajectory tracking algorithm. In this proposed method, the longitudinal control system is developed based on the curvature information of the reference path. The autonomous vehicle modifies the desired speed according to the estimated size and types of the reference trajectory curves. This desired speed is integrated into the PID controller to maintain an optimal speed of the vehicle while following the given path. The lateral control system is designed based on feedforward (preview control) and feedback (LQR) controllers to reduce lateral errors between the trajectory and autonomous vehicle. The feedforward and the feedback controllers generate precise steering angles to eliminate orientation and lateral errors caused by the curvature of the trajectory and external disturbances. The effectiveness of the proposed method is evaluated by comparing simulation and experimental results with different trajectory tracking algorithms on simulated and experimented paths. It is proven that the proposed algorithm is capable of significantly minimizing lateral errors on sharp curves compared to other path tracking methods.
Publisher: Elsevier BV
Date: 02-2019
Publisher: Elsevier BV
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: IEEE
Date: 22-10-2021
Publisher: IEEE
Date: 06-2013
Publisher: Elsevier BV
Date: 07-2017
Publisher: MDPI AG
Date: 04-12-2022
Abstract: Visual Place Recognition (VPR) is a fundamental yet challenging task in Visual Simultaneous Localization and Mapping (V-SLAM) problems. The VPR works as a subsystem of the V-SLAM. VPR is the task of retrieving images upon revisiting the same place in different conditions. The problem is even more difficult for agricultural and all-terrain autonomous mobile robots that work in different scenarios and weather conditions. Over the last few years, many state-of-the-art methods have been proposed to solve the limitations of existing VPR techniques. VPR using bag-of-words obtained from local features works well for a large-scale image retrieval problem. However, the aggregation of local features arbitrarily produces a large bag-of-words vector database, limits the capability of efficient feature learning, and aggregation and querying of candidate images. Moreover, aggregating arbitrary features is inefficient as not all local features equally contribute to long-term place recognition tasks. Therefore, a novel VPR architecture is proposed suitable for efficient place recognition with semantically meaningful local features and their 3D geometrical verifications. The proposed end-to-end architecture is fueled by a deep neural network, a bag-of-words database, and 3D geometrical verification for place recognition. This method is aware of meaningful and informative features of images for better scene understanding. Later, 3D geometrical information from the corresponding meaningful features is computed and utilised for verifying correct place recognition. The proposed method is tested on four well-known public datasets, and Micro Aerial Vehicle (MAV) recorded dataset for experimental validation from Victoria Park, Adelaide, Australia. The extensive experimental results considering standard evaluation metrics for VPR show that the proposed method produces superior performance than the available state-of-the-art methods.
Publisher: IEEE
Date: 12-2015
Publisher: AIP Publishing
Date: 03-2014
DOI: 10.1063/1.4868249
Abstract: This paper demonstrates a high-speed spiral imaging technique for an atomic force microscope (AFM). As an alternative to traditional raster scanning, an approach of gradient pulsing using a spiral line is implemented and spirals are generated by applying single-frequency cosine and sine waves of slowly varying litudes to the X and Y-axes of the AFM’s piezoelectric tube scanner (PTS). Due to these single-frequency sinusoidal input signals, the scanning process can be faster than that of conventional raster scanning. A linear quadratic Gaussian controller is designed to track the reference sinusoid and a vibration compensator is combined to d the resonant mode of the PTS. An internal model of the reference sinusoidal signal is included in the plant model and an integrator for the system error is introduced in the proposed control scheme. As a result, the phase error between the input and output sinusoids from the X and Y-PTSs is reduced. The spirals produced have particularly narrow-band frequency measures which change slowly over time, thereby making it possible for the scanner to achieve improved tracking and continuous high-speed scanning rather than being restricted to the back and forth motion of raster scanning. As part of the post-processing of the experimental data, a fifth-order Butterworth filter is used to filter noises in the signals emanating from the position sensors and a Gaussian image filter is used to filter the images. A comparison of images scanned using the proposed controller (spiral) and the AFM PI controller (raster) shows improvement in the scanning rate using the proposed method.
Publisher: IEEE
Date: 06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2014
Publisher: IEEE
Date: 08-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2013
Publisher: IEEE
Date: 08-2013
Location: Bangladesh
No related grants have been discovered for Habibullah Habibullah.