KN-LIO：几何运动学与神经场耦合的激光雷达-惯性里程计
KN-LIO: Geometric Kinematics and Neural Field Coupled LiDAR-Inertial Odometry
AI总结：
针对高动态场景下传统LIO建图稀疏、纯激光雷达神经场建图困难的问题，提出几何运动学与神经场耦合的KN-LIO，实现高精度状态估计与稠密建图。
摘要：
激光雷达-惯性里程计（LIO）的最新进展推动了大量应用。然而，传统LIO系统往往更关注定位而非建图，其地图主要由稀疏几何元素组成，对下游任务并不理想。近期兴起的神经场技术在稠密建图方面具有巨大潜力，但纯激光雷达建图难以在高动态载体上工作。为缓解这一挑战，我们提出一种将几何运动学与神经场紧耦合的新方案，以增强同步状态估计和稠密建图能力。我们提出了半耦合和紧耦合两种Kinematic-Neural 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。我们的KN-LIO最大限度减少了信息损失，提高了状态估计精度，同时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，我们的KN-LIO在位姿估计方面取得了与现有最先进方案相当或更优的性能，并且相比纯基于激光雷达的方法提供了更高的稠密建图精度。相关代码和数据集将在https://**公开。
英文摘要：
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://**.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
