Recognising, Representing and Mapping Natural Features in Unstructured Environments

This thesis addresses the problem of building statistical models for multi-sensor perception in unstructured outdoor environments. The perception problem is divided into three distinct tasks: recognition, representation and association. Recognition is cast as a statistical classification problem where inputs are images or a combination of images and ranging information. Given the complexity and variability of natural environments, this thesis investigates the use of Bayesian statistics and supervised dimensionality reduction to incorporate prior information and fuse sensory data. A compact probabilistic representation of natural objects is essential for many problems in field robotics. This thesis presents techniques for combining non-linear dimensionality reduction with parametric learning through Expectation Maximisation to build general representations of natural features. Once created these models need to be rapidly processed to account for incoming information. To this end, techniques for efficient probabilistic inference are proposed. The robustness of localisation and mapping algorithms is directly related to reliable data association. Conventional algorithms employ only geometric information which can become inconsistent for large trajectories. A new data association algorithm incorporating visual and geometric information is proposed to improve the reliability of this task. The method uses a compact probabilistic representation of objects to fuse visual and geometric information for the association decision. The main contributions of this thesis are: 1) a stochastic representation of objects through non-linear dimensionality reduction; 2) a landmark recognition system using a visual and ranging sensors; 3) a data association algorithm combining appearance and position properties; 4) a real-time algorithm for detection and segmentation of natural objects from few training images and 5) a real-time place recognition system combining dimensionality reduction and Bayesian learning. The theoretical contributions of this thesis are demonstrated with a series of experiments in unstructured environments. In particular, the combination of recognition, representation and association algorithms is applied to the Simultaneous Localisation and Mapping problem (SLAM) to close large loops in outdoor trajectories, proving the benefits of the proposed methodology

Towards Sustainable Oceans: Deep Learning Models for Accurate COTS Detection in Underwater Images

Object detection is one of the main tasks in computer vision, which includes image classification and localization. The application of object detection is now widespread as it powers various applications such as self-driving cars, robotics, biometrics, surveillance, satellite image analysis, and in healthcare, to mention just a few. Deep learning has taken computer vision to a different horizon. One of the areas that will benefit immensely from deep learning computer vision is the detection of killer starfish, the crown-of-thorns starfish (COTS). For decades, this killer starfish has dealt a big blow to the Great Barrier Reef in Australia, the world’s largest system of reefs, and in other places too. In addition to impacting negatively environmentally, it affects revenue generation from reef tourism. Hence, reef managers and authorities want to control the populations of crown-of-thorns starfish, which have been observed to be the culprits. The deep learning technique offers real-time and robust detection of this creature more than earlier traditional methods that were used to detect these creatures. This thesis work is part of a competition for a deep learning approach to detect COTS in real time by building an object detector trained using underwater images. This offers a solution to control the outbreaks in the population of these animals. Deep learning methods of Artificial Intelligence (AI) have gained popularity today because of its speed and high accuracy in detection and have performed better than the earlier traditional methods. They can be used in real-time object detection, and they owe their speed to convolutional neural networks (CNN). The thesis gives a comprehensive literature review of the journey so far in the field of computer vision and how deep learning methods can be applied to detect COTS. It also outlines the steps involved in the implementation of the model using the state-of-the-art computer vision algorithm known for its speed and accuracy – YOLOv8. The COTS detection model was trained using the custom dataset provided by the organizers of the competition, harnessing the powers of deep learning methods such as transfer learning, data augmentation, and preprocessing of underwater images to achieve high accuracy. Evaluation of the results obtained from the training showed a mean average precision of 0.803mAP at IoU of 0.5-0.95, acknowledging the detector model’s versatility in making accurate detection at different confidence levels. This supports the hypothesis that when we use pre trained model, this enhances the performance of our model for better object detection tasks. Certainly, better detection accuracy is one way to detect killer starfish, the crown-of-thorns starfish (COTS), and help protect the oceans