GenZ-LIO: Generalizable LiDAR-Inertial Odometry Beyond Indoor--Outdoor Boundaries
🤖 AI Summary
This work addresses the degradation in robustness and efficiency of LiDAR-inertial odometry during transitions between indoor and outdoor environments, where drastic changes in point cloud density pose significant challenges. To this end, the authors propose GenZ-LIO, a novel framework that adaptively adjusts voxel size through a PID-like feedback mechanism to align with scene scale. It integrates point-to-point and point-to-plane residuals into a hybrid geometric constraint formulation to mitigate degeneracy, and employs a voxel pruning strategy to accelerate correspondence search. The resulting tightly coupled state estimation system demonstrates consistent improvements in both pose accuracy and computational efficiency across diverse scenarios—including indoor, outdoor, and transitional environments—without requiring manual parameter tuning.
Application Category
📝 Abstract
Light detection and ranging (LiDAR)-inertial odometry (LIO) enables accurate localization and mapping for autonomous navigation in various scenes. However, its performance remains sensitive to variations in spatial scale, which refers to the spatial extent of the scene reflected in the distribution of point ranges in a LiDAR scan. Transitions between confined indoor and expansive outdoor spaces induce substantial variations in point density, which may reduce robustness and computational efficiency. To address this issue, we propose GenZ-LIO, a LIO framework generalizable across both indoor and outdoor environments. GenZ-LIO comprises three key components. First, inspired by the principle of the proportional-integral-derivative (PID) controller, it adaptively regulates the voxel size for downsampling via feedback control, driving the voxelized point count toward a scale-informed setpoint while enabling stable and efficient processing across varying scene scales. Second, we formulate a hybrid-metric state update that jointly leverages point-to-plane and point-to-point residuals to mitigate LiDAR degeneracy arising from directionally insufficient geometric constraints. Third, to alleviate the computational burden introduced by point-to-point matching, we introduce a voxel-pruned correspondence search strategy that discards non-promising voxel candidates and reduces unnecessary computations. Experimental results demonstrate that GenZ-LIO achieves robust odometry estimation and improved computational efficiency across confined indoor, open outdoor, and transitional environments. Our code will be made publicly available upon publication.
Problem
Research questions and friction points this paper is trying to address.
LiDAR-inertial odometry
spatial scale variation
point density
indoor-outdoor transition
robustness
Innovation
Methods, ideas, or system contributions that make the work stand out.
adaptive voxelization
hybrid-metric state update
voxel-pruned correspondence
scale-invariant LIO
LiDAR-inertial odometry
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