Browsing by Author "Strauss, March"

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    Robust place recognition for vision-based SLAM systems using semantic information.
    (Stellenbosch : Stellenbosch University, 2024-03) Strauss, March; Van Daalen, Corne E. ; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: When constructing a map of an unknown environment, it is important to be able to recognise when you have encountered a previously visited area. In visual simultaneous localisation and mapping (SLAM), this is known as the ‘revisiting problem’, and it is important for such ‘loops’ in a robotic vehicle’s trajectory to be detected and closed, as doing so allows for estimations of said trajectory to be improved, by removing any drift that has accumulated over time. While many methods for performing loop closure exist for vision-based robots, they rarely make use of the context of objects and features present in a scene, the so called ‘semantic content’. This is unlike human beings, who recognise environments by the presence of, and relationships between, objects and landmarks. In order to address this lack of spatial-semantic awareness, this thesis proposes a system that can describe a scene based on both its spatial structure and semantic content. Such a system allows for different scenes to be compared and matched with one another, allowing for loops to be detected and closed. The proposed solution makes use of a landmark-based 3D representation of an observed environment measured using stereo cameras, combined with semantic information found using a convolutional neural network. These aspects are combined to form the proposed scene descriptor, dubbed semantic multiview 2D projection (SeM2DP). The descriptor is then evaluated against a number of existing visual place recognition algorithms, in order to gauge its performance in terms of accuracy and processing speed. The results of these experiments show that the proposed solution has comparable to better accuracy than other existing solutions, while requiring a smaller descriptor. The main cost of using the proposed algorithm is its relatively slow computational performance, though the other tested methods were found to only around 50% faster to compute than SeM2DP. These results show that the proposed solution is well suited to being used in landmarkbased visual SLAM systems. SeM2DP is also the better method to use if semantic data is being recorded for purposes beyond just improving navigation accuracy, as this offsets the main drawback of using SeM2DP over other existing methods.