Application of deep learning for enhancing simultaneous localization and mapping in autonomous driving

• Main Author: Ge, Jintian
• Other Authors: Lyu Chen
• Format: Thesis-Master by Research
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Engineering; Deep learning; SLAM; Autonomous driving
• Online Access: https://hdl.handle.net/10356/174794
• Institution: Nanyang Technological University

Simultaneous Localization and Mapping, commonly referred to as SLAM, represents a class of algorithms that involves constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's position within it. This technique is foundational in robotics and autonomous systems, enabling machines and robots to explore, understand, and navigate their surroundings. Deep learning, characterized by its prowess in handling vast data and intricate patterns, has shown promise in several Computer Vision (CV) tasks, i.e., semantic segmentation, optical flow estimation, and disparity estimation. Deep learning excels at these tasks, delivering dense and accurate results. However, as a downstream work of perception, SLAM pays little attention to the application of these results. Therefore, this paper aims to explore possible applications of deep learning (especially CV tasks) in SLAM tasks. In this thesis, we delve into the confluence of Deep Learning and SLAM tasks, emphasizing the potential for performance enhancement and methodological innovation. Our exploration is structured around four SLAM related tasks, each distinct yet synergistically linked around a center topic "application of deep learning to enhance performance": an automatic calibration approach for radar-camera system as a pre-process of multi-modal SLAM, which leverages deep learning models to eliminate the need for special markers; the application of deep learning to stereo 3D reconstruction through disparity estimation, presenting its potential for dense environment local mapping; the utility of semantics in initial pose estimation and feature matching within visual odometry for localization, refining the precision of positioning techniques; and introduction of supervised contrastive learning for image representation, targeting the challenges associated with fine-tuning deep learning models in real-world deployments. Collectively, these tasks underline the potential of deep learning in advancing robotic methodologies and offer a vision for future research directions.