The recent advancements in Information and Communication Technology (ICT) as well as increasing demand for vehicular safety has led to significant progressions in Autonomous Vehicle (AV) technology. Perception and Localisation are major operations that determine the success of AV development and usage. Therefore, significant research has been carried out to provide AVs with the capabilities to not only sense and understand their surroundings efficiently, but also provide detailed information of the environment in the form of 3D maps. Visual Simultaneous Localisation and Mapping (V-SLAM) has been utilised to enable a vehicle understand its surroundings, map the environment, and identify its position within the area. This paper presents a detailed review of V-SLAM techniques implemented for AV perception and localisation. An overview of SLAM techniques is presented. In addition, an in-depth review is conducted to highlight various V-SLAM schemes, their strengths, and limitations. Challenges associated with V-SLAM deployment and future research directions are also provided in this paper.

The proliferation of autonomous vehicles (AVs) emphasises the pressing need to navigate challenging road networks riddled with anomalies like unapproved speed bumps, potholes, and other hazardous conditions, particularly in low- and middle-income countries. These anomalies not only contribute to driving stress, vehicle damage, and financial implications for users but also elevate the risk of accidents. A significant hurdle for AV deployment is the vehicle’s environmental awareness and the capacity to localise effectively without excessive dependence on pre-defined maps in dynamically evolving contexts. Addressing this overarching challenge, this paper introduces a specialised deep learning model, leveraging YOLO v4, which profiles road surfaces by pinpointing defects, demonstrating a mean average precision (mAP@0.5) of 95.34%. Concurrently, a comprehensive solution—RA-SLAM, which is an enhanced Visual Simultaneous Localisation and Mapping (V-SLAM) mechanism for road scene modeling, integrated with the YOLO v4 algorithm—was developed. This approach precisely detects road anomalies, further refining V-SLAM through a keypoint aggregation algorithm. Collectively, these advancements underscore the potential for a holistic integration into AV’s intelligent navigation systems, ensuring safer and more efficient traversal across intricate road terrains.

The application of precision agriculture in farming practices results in higher yield and productivity with lower costs. Several works have applied this concept to poultry farming to reduce human involvement, stress, fatigue, wastage of poultry feed and provide a high return on investment. A number of these systems lack control techniques to improve the system performance. A few works exist that implemented control techniques to improve system response, but different systems were implemented and therefore, a comparison cannot be made. In this paper the performance comparison of the Fuzzy Logic Controller (FLC) and the PID Controller on the Poultry Feed Dispensing System was evaluated in a quest to determine the more efficient and effective controller. The system was modelled and simulated using MATLAB SIMULINK and the performance was evaluated based on the rise time, settling time, overshoot and Integrated Absolute Error (IAE). The results showed that the system implemented with the PID and FLC performed better than the system without a control technique. The PID gave a faster system response than the FLC in the solid feed subsystem with a difference in rise time, settling time and IAE of 9.72 seconds, 11.68 seconds and 4.74 respectively. The FLC performed better in the liquid feed subsystem with a difference in rise time, settling time, overshoot and IAE of 9.22 seconds, 33.07 seconds, 13.92% and 7.18 respectively. This shows that the PID controller is more suitable in the solid feed subsystem and the FLC is more effective in the liquid feed subsystem.

Agriculture and agribusinesses suffer from many challenges, despite their significance to global economic growth. One of the challenges is the lack of appropriate technology to drive the industry to the next level of development. This technological gap contributes to reduced yield and profit without a reduction in manual labour, cost, and stress. Robotics have been explored to boost agricultural production and improve agribusiness productivity. Several weed control robots have been developed for research and field uses, but these systems are not suitable for weed control in large commercial farms or lack control schemes for navigation and weed control. This study presents the design of an autonomous robot system for chemical weed control. The system uses control theory, artificial intelligence, and image processing to navigate a farm environment, identify weeds, and apply herbicide where necessary. Upon implementation and adoption, this system would increase agricultural productivity with minimal human input, thereby leading to an increase in revenue and profit for agribusinesses.