Abstract
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://**.
Abstract (translated)
最近在LiDAR惯性里程计(LIO)领域的进展极大地促进了各种应用的发展。然而,传统的LIO系统通常更侧重于定位而非地图构建,生成的地图主要由稀疏的几何元素组成,这对下游任务来说不够理想。新兴的神经场技术在密集地图构建方面具有巨大潜力,但纯LiDAR地图构建对于高动态车辆而言较为困难。为了解决这一挑战,我们提出了一种新的解决方案,即紧密集成几何动力学与神经场以增强同时状态估计和密集地图构建的能力。我们提出了半耦合及紧耦合的动力学-神经LIO(KN-LIO)系统,该系统利用在线SDF解码以及迭代误差状态卡尔曼滤波器来融合激光数据和惯性数据。 我们的KN-LIO技术减少了信息损失,并在状态估计的准确性上有所提升。同时,它也能适应异步多LiDAR输入的情况。在多种高动态数据集上的评估结果表明,与现有的最先进的解决方案相比,我们的KN-LIO系统在姿态估计方面达到了同等或更高的性能水平,并且相对于纯LiDAR方法,在密集地图构建的精确度上有所提高。 相关代码和数据集将在https://**(实际网址需要替换)提供。
URL
https://arxiv.org/abs/2501.04263