将激光雷达场景的位置识别转为分类任务，提升训练效率与稳定性。
Towards classification-based representation learning for place recognition on LiDAR scans
- 将激光雷达扫描图直接分类为具体位置，用编码器-解码器模型训练。
- 在NuScenes数据集上性能媲美对比学习方法，训练速度更快。
- 适合追求高效稳定训练的自动驾驶定位系统开发者。
位置识别是自动驾驶中的关键任务，使车辆能通过传感器数据确定自身位置。现有方法多依赖对比学习，本文提出新思路：将位置识别建模为多分类问题。通过为激光雷达扫描分配离散位置标签，训练编码器-解码器模型直接进行分类。在NuScenes数据集上的实验表明，该方法性能可媲美基于对比学习的方法，同时在训练效率和稳定性方面更具优势。
原文摘要 · Abstract (English)
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.
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