Lightweight Mapping of Unstructured Environments - Katsoulis, Michael (Department of Electrical Engineering, 2024)
We present a computationally, size and cost-lightweight monocular reconstruction pipeline that produces high-quality reconstructions in an unstructured agricultural environment consisting of orchards and vineyards. The pipeline has to be deployed and tested on ground vehicles equipped only with a single monocular camera sensor. Running on a CPU only, while achieving a sufficient resolution to identify individual plants without limitations on maximum path length. We show that a simple visual odometry system is capable of providing performance that is more accurate than GPS, with a relative transform error of 0.19m, without the need for techniques such as bundle adjustment or loop closure. Towards this contribution, we evaluate the impact of the choice of image feature on the accuracy of the visual odometry as well as the impact of the choice of disparity estimation method on the accuracy. Additionally, we show that state-of-the-art unsupervised monocular depth networks can outperform stereo techniques in terms of accuracy achieved when estimating the depth in an agricultural setting. We also show that lightweight pyramid-based methods are able to match the performance of deep monocular depth networks at the task of disparity estimation. The pipeline presented is optimised for application in agricultural environments and is lightweight in terms of size, weight, power and computational requirements. The pipeline functions using only a single camera and without any other sensors or sources of additional information making