将运动学与神经场结合，实现高动态下精准定位与稠密建图。
KN-LIO: Geometric Kinematics and Neural Field Coupled LiDAR-Inertial Odometry
- 融合几何运动学与神经场，用实时SDF解码提升建图密度
- 在高动态数据集上定位精度优于或媲美现有最优方法
- 支持异步多激光雷达输入，适合自动驾驶等复杂场景
近年来，激光雷达-惯性里程计（LIO）推动了大量应用发展。然而传统LIO系统更侧重定位而非建图，地图多由稀疏几何元素构成，不利于下游任务。新兴的神经场技术在稠密建图方面潜力巨大，但纯激光雷达方法在高动态车辆上难以奏效。为此，我们提出一种紧耦合几何运动学与神经场的新方案，构建半耦合与紧耦合的运动-神经LIO（KN-LIO）系统，利用在线SDF解码和迭代误差状态卡尔曼滤波融合激光与惯性数据。KN-LIO有效减少信息损失，提升状态估计精度，并支持异步多激光雷达输入。在多种高动态数据集上的评估表明，其姿态估计性能达到或超过现有最先进水平，且稠密建图精度显著优于纯激光雷达方法。相关代码与数据集将公开于 https://**。
原文摘要 · Abstract (English)
Recent advancements in LiDAR-Inertial Odometry (LIO) have boosted a large amount of applications. However, traditional LIO systems tend to focus more on localization rather than mapping, with maps consisting mostly of sparse geometric elements, which is not ideal for downstream tasks. Recent emerging neural field technology has great potential in dense mapping, but pure LiDAR mapping is difficult to work on high-dynamic vehicles. To mitigate this challenge, we present a new solution that tightly couples geometric kinematics with neural fields to enhance simultaneous state estimation and dense mapping capabilities. We propose both semi-coupled and tightly coupled Kinematic-Neural LIO (KN-LIO) systems that leverage online SDF decoding and iterated error-state Kalman filtering to fuse laser and inertial data. Our KN-LIO minimizes information loss and improves accuracy in state estimation, while also accommodating asynchronous multi-LiDAR inputs. Evaluations on diverse high-dynamic datasets demonstrate that our KN-LIO achieves performance on par with or superior to existing state-of-the-art solutions in pose estimation and offers improved dense mapping accuracy over pure LiDAR-based methods. The relevant code and datasets will be made available at https://**.
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