As a crucial infrastructure of intelligent mobile robots, LiDAR-Inertial odometry (LIO) provides the basic capability of state estimation by tracking LiDAR scans. The high-accuracy tracking generally involves the kNN search, which is used with minimizing the point-to-plane distance. The cost for this, however, is maintaining a large local map and performing kNN plane fit for each point. In this work, we reduce both time and space complexity of LIO by saving these unnecessary costs. Technically, we design a plane pre-fitting (PPF) pipeline to track the basic skeleton of the 3D scene. In PPF, planes are not fitted individually for each scan, let alone for each point, but are updated incrementally as the scene 'flows'. Unlike kNN, the PPF is more robust to noisy and non-strict planes with our iterative Principal Component Analyse (iPCA) refinement. Moreover, a simple yet effective sandwich layer is introduced to eliminate false point-to-plane matches. Our method was extensively tested on a total number of 22 sequences across 5 open datasets, and evaluated in 3 existing state-of-the-art LIO systems. By contrast, LIO-PPF can consume only 36% of the original local map size to achieve up to 4x faster residual computing and 1.92x overall FPS, while maintaining the same level of accuracy. We fully open source our implementation at https://github.com/xingyuuchen/LIO-PPF.
翻译:作为智能移动机器人的关键基础设施,LIDAR-Intertial odology(LIO)通过跟踪 LiDAR 扫描为国家估算提供基本能力。 高精确度跟踪一般涉及 kNN 搜索, 用于将点对平面距离最小化。 但是, 这样做的成本是维护大局地图, 并运行适合每个点的 kNN 平面。 在这项工作中, 我们通过节省这些不必要的费用, 减少了LIO的时间和空间复杂性。 技术上, 我们设计了一台飞机预装( PPFF) 管道, 以跟踪3D 场的基本骨架。 在 PPFPF中, 飞机不是单独安装给每次扫描的, 更不用说每个点, 而是作为场景的“ 流动” 。 与 kNNN不同的是, PPFPF 维护了更强的噪音和非约束性飞机。 此外, 我们引入了一个简单有效的三明治层, 来消除错误的点对平面点对平面的匹配。 我们的方法在5个开放数据集/ 的22个序列上进行了广泛测试,,, 更不用说 的LPFPFPFS- 3 的精确度, 的原始级, 只能 和整个 进行对比, 的LPFPFPFPFPFPF- 3 级 的进度 的比 3 的比 的精确度只能 。</s>