Benchmarking Visual Feature Representations for LiDAR-Inertial-Visual Odometry Under Challenging Conditions

Accurate localization in autonomous driving is critical for successful missions including environmental mapping and survivor searches. In visually challenging environments, including low-light conditions, overexposure, illumination changes, and high parallax, the performance of conventional visual odometry methods significantly degrade undermining robust robotic navigation. Researchers have recently proposed LiDAR-inertial-visual odometry (LIVO) frameworks, that integrate LiDAR, IMU, and camera sensors, to address these challenges. This paper extends the FAST-LIVO2-based framework by introducing a hybrid approach that integrates direct photometric methods with descriptor-based feature matching. For the descriptor-based feature matching, this work proposes pairs of ORB with the Hamming distance, SuperPoint with SuperGlue, SuperPoint with LightGlue, and XFeat with the mutual nearest neighbor. The proposed configurations are benchmarked by accuracy, computational cost, and feature tracking stability, enabling a quantitative comparison of the adaptability and applicability of visual descriptors. The experimental results reveal that the proposed hybrid approach outperforms the conventional sparse-direct method. Although the sparse-direct method often fails to converge in regions where photometric inconsistency arises due to illumination changes, the proposed approach still maintains robust performance under the same conditions. Furthermore, the hybrid approach with learning-based descriptors enables robust and reliable visual state estimation across challenging environments.

GenZ-LIO: Generalizable LiDAR-Inertial Odometry Beyond Indoor--Outdoor Boundaries

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.