2025/11/01 by Maksim Konoplia, Dmitrii Khizbullin, Konoplia, Maksim +1
Computer Science · Engineering · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.2511.00738
openalex publication_date 2025/11/01 · openalex created_date 2025/11/06 · openalex updated_date 2026/07/28
Place recognition is a crucial task in autonomous driving, allowing vehicles to determine their position using sensor data. While most existing methods rely on contrastive learning, we explore an alternative approach by framing place recognition as a multi-class classification problem. Our method assigns discrete location labels to LiDAR scans and trains an encoder-decoder model to classify each scan's position directly. We evaluate this approach on the NuScenes dataset and show that it achieves competitive performance compared to contrastive learning-based methods while offering advantages in training efficiency and stability.