Cubemap-Based LiDAR-Inertial Odometry with Intensity Assistance

Abstract

We present CUBE-LIO, a LiDAR-inertial odometry framework that leverages direct photometric constraints from LiDAR intensity to improve robustness in geometrically degenerate environments. At its core is an efficient cubemap projection that maps LiDAR intensity onto six cube faces, eliminating pole singularities and severe polar distortion. This yields a more uniform overall distortion while avoiding the costly trigonometric operations typical of equirectangular mappings. Building on this representation, we introduce a semi-dense feature selection and direct optimization strategy based on intensity gradient magnitude. This strategy improves resilience to intensity noise and variations induced by range and incidence angle. Photometric constraints are jointly optimized with geometric measurements in a tightly coupled LIO pipeline. CUBE-LIO is sensor-agnostic and supports both spinning and solid-state LiDARs. Experiments on multiple public benchmarks demonstrate state-of-the-art accuracy and real-time performance, with particularly pronounced gains in scenes where the geometric structure is sparse or weak.