Elevator-LIO: Robust LiDAR-Inertial Odometry for Multi-Floor Navigation under Elevator-Induced Non-Inertial Motion

2026-05-23

AI Summary

This work addresses the challenges of robot localization disruption and vertical drift caused by non-inertial motion during elevator rides. To tackle these issues, the authors propose a decoupled state estimation model that separately represents the robot’s motion relative to the elevator and the elevator’s own motion, embedded within a mode-dependent iterative error-state Kalman filtering framework. A novel elevator mode manager is introduced, which automatically detects elevator entry and exit events using LiDAR range statistics and triggers zero-velocity and zero-acceleration updates during stops to suppress altitude drift. Additionally, adaptive voxel downsampling is employed to handle drastic environmental scale changes. Evaluated on 20 real-world sequences encompassing 79 elevator rides, the method achieves continuous localization with terminal height errors below 1 cm in 17 sequences, significantly outperforming existing approaches while maintaining competitive performance in standard indoor environments.

Abstract

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/.

Problem

• LiDAR-inertial odometry
• elevator-induced non-inertial motion
• multi-floor navigation
• state estimation
• vertical drift

Innovation

• LiDAR-inertial odometry
• non-inertial motion
• decoupled state estimation
• elevator mode detection
• adaptive voxel downsampling