Elevator-LIO：电梯引起的非惯性运动下多层导航的鲁棒激光雷达-惯性里程计
Elevator-LIO: Robust LiDAR-Inertial Odometry for Multi-Floor Navigation under Elevator-Induced Non-Inertial Motion
AI总结：
提出Elevator-LIO框架，通过解耦状态估计模型和模式依赖的迭代误差状态卡尔曼滤波器，实现电梯内连续定位，并利用自适应体素降采样和事件触发更新抑制垂直漂移。
摘要：
本文提出了Elevator-LIO，一种旨在电梯行驶过程中实现机器人连续定位的激光雷达-惯性里程计框架，从而支持跨楼层机器人任务。为了解决非惯性框架下的状态估计问题，Elevator-LIO建立了一个解耦的状态估计模型，分别对机器人相对于电梯的运动和电梯自身的运动进行建模，并将其嵌入到模式依赖的迭代误差状态卡尔曼滤波器框架中。该框架在普通室内环境中退化为常规LIO估计，同时在电梯非惯性环境中实现电梯相关状态的传播和约束更新，从而实现连续稳定的定位。电梯模式管理器利用激光雷达测距统计和估计状态检测电梯进出事件，并在电梯停止时引入事件触发的零速度和零加速度更新，以抑制累积的垂直漂移。此外，本文采用自适应体素降采样策略，在环境尺度显著变化时保持有效点数的稳定。我们在包含79次电梯乘坐的20个真实世界序列上进行了广泛实验，包括大尺度空间、长垂直行程、动态行人干扰和镜面反射等实际挑战。结果表明，Elevator-LIO在所有序列中保持连续定位精度，其中17个序列的终端高度误差低于1厘米。相比之下，现有代表性定位系统在这些电梯序列上表现不佳。在Hilti 2022/2023数据集上的测试进一步表明，所提方法在标准室内场景中仍具有竞争力。项目页面位于https://xiaofan4122.github.io/Elevator_LIO_Page/。
英文摘要：
This paper presents Elevator-LIO, a LiDAR-inertial odometry framework designed to achieve continuous robot localization during elevator travel, thereby supporting cross-floor robotic tasks. To address the state-estimation problem in non-inertial frames, Elevator-LIO establishes a decoupled state-estimation model that separately models the robot motion relative to the elevator and the elevator motion itself, and embeds it into a mode-dependent iterated error-state Kalman filter framework. This framework degenerates to conventional LIO estimation in ordinary indoor environments, while enabling the propagation and constrained update of elevator-related states in elevator non-inertial environments, thereby achieving continuous and stable localization. An elevator mode manager detects elevator entry and exit events using LiDAR ranging statistics and estimated states, and introduces event-triggered zero-velocity and zero-acceleration updates when the elevator stops to suppress accumulated vertical drift. In addition, this paper adopts an adaptive voxel downsampling strategy to maintain a stable number of effective points under significant environmental scale changes. We conduct extensive experiments on 20 real-world sequences containing 79 elevator rides, including practical challenges such as large-scale spaces, long vertical travel, dynamic pedestrian interference, and mirror reflections. The results show that Elevator-LIO maintains continuous localization accuracy in all sequences, with terminal height error below 1 cm in 17 sequences. In contrast, existing representative localization systems perform poorly on these elevator sequences. Tests on the Hilti 2022/2023 datasets further show that the proposed method remains competitive in standard indoor scenarios. The project page is available at https://xiaofan4122.github.io/Elevator_LIO_Page/.
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。
