Campus Autonomy: Navigating the Future with Autonomous Indoor-Outdoor Delivery Vehicles
Development of an autonomous delivery vehicle capable of navigating both indoor and outdoor environments within a campus setting.
Development of an autonomous delivery vehicle capable of navigating both indoor and outdoor environments within a campus setting.
Enhancing navigation safety in deep reinforcement learning agents through reward shaping with prior map information.
A LiDAR-based perception system for automated solar panel installation, featuring real-time detection of solar racks and support structures.
Development of a SLAM system with vertical plane segmentation for creating long-term indoor maps.
Published in arXiv, 2024
This study enhances robot navigation by integrating Large Language Models (LLMs) with ROS move_base, using osmAG for semantic mapping. The approach combines human-like contextual understanding with traditional navigation, improving adaptability and robustness.
Recommended citation: @misc{xie2024intelligentlidarnavigationleveraging, title={Intelligent LiDAR Navigation: Leveraging External Information and Semantic Maps with LLM as Copilot}, author={Fujing Xie and Jiajie Zhang and Sören Schwertfeger}, year={2024}, eprint={2409.08493}, archivePrefix={arXiv}, primaryClass={cs.RO}, url={https://arxiv.org/abs/2409.08493}, }
Download Paper
Published in Sensors (Basel, Switzerland), 2024
This paper introduces a novel neural surfel-based method for large-scale 3D scene reconstruction, addressing challenges in loop closure and bundle adjustment. By integrating neural descriptors with surfels and optimizing surfel associations, the approach achieves improved reconstruction accuracy and significantly reduces file sizes compared to traditional methods.
Recommended citation: Cui J, Zhang J, Kneip L, Schwertfeger S. Neural Surfel Reconstruction: Addressing Loop Closure Challenges in Large-Scale 3D Neural Scene Mapping. Sensors (Basel). 2024 Oct 28;24(21):6919. doi: 10.3390/s24216919. PMID: 39517816; PMCID: PMC11548607.
Download Paper
Graduate course, ShanghaiTech University, SIST, 2024