Place recognition for unmanned vehicle based on 3D LiDAR and semantic point cloud - Zhao, Yangyang (Nanyang Technological University, 2024)
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.