Place recognition for unmanned vehicle based on 3D LiDAR and semantic point cloud

• Main Author: Zhao, Yangyang
• Other Authors: Wang Dan Wei
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
• Online Access: https://hdl.handle.net/10356/173189
• Institution: Nanyang Technological University

Place recognition is an important task in the computer vision and robotics communities, with a wild application in many fields. For unmanned vehicle, 3D LiDAR and semantic point cloud is always used for place recognition. Recent state-of-the-art works mostly focus on structural design of the network. This dissertation introduces relation-based and response-based self-knowledge distillation into the training process and further proposes an instance-to-region supervised knowledge distillation method based on MinkLoc3Dv2 backbone. Experimental evaluation shows excellent performance and generalization on standard benchmarks.