提出分层姿态估计方法，实现大规模神经隐式地图的精准定位与稳定建图。
Hierarchical Pose Estimation and Mapping with Multi-Scale Neural Feature Fields
- 基于概率视角构建分层姿态估计框架，结合多尺度神经特征场
- 在KITTI和MaiCity数据集上实现高精度位姿估计与稳定映射
- 适用于未知传感器位姿的大规模户外激光雷达场景重建
机器人应用需要对环境有全面理解。近年来，基于神经场的方法通过参数化整个环境而变得流行，因其连续性及学习场景先验的能力而具有前景。然而，在处理未知传感器位姿和序列测量时，神经场在机器人中的应用面临挑战。本文聚焦于大规模神经隐式SLAM中的传感器位姿估计问题，从概率角度研究隐式建图，并提出相应的分层姿态估计方法与神经网络架构。该方法适用于大规模隐式地图表示。所提方法处理连续室外激光雷达扫描，实现了精确位姿估计，同时在短程和长程轨迹上均保持稳定的映射质量。我们在适合大规模重建的结构化稀疏隐式表示基础上构建方法，并在KITTI和MaiCity数据集上进行评估。实验表明，该方法在未知位姿条件下优于基线，达到当前最优的定位精度。
原文摘要 · Abstract (English)
Robotic applications require a comprehensive understanding of the scene. In recent years, neural fields-based approaches that parameterize the entire environment have become popular. These approaches are promising due to their continuous nature and their ability to learn scene priors. However, the use of neural fields in robotics becomes challenging when dealing with unknown sensor poses and sequential measurements. This paper focuses on the problem of sensor pose estimation for large-scale neural implicit SLAM. We investigate implicit mapping from a probabilistic perspective and propose hierarchical pose estimation with a corresponding neural network architecture. Our method is well-suited for large-scale implicit map representations. The proposed approach operates on consecutive outdoor LiDAR scans and achieves accurate pose estimation, while maintaining stable mapping quality for both short and long trajectories. We built our method on a structured and sparse implicit representation suitable for large-scale reconstruction and evaluated it using the KITTI and MaiCity datasets. Our approach outperforms the baseline in terms of mapping with unknown poses and achieves state-of-the-art localization accuracy.
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