A Pose-Only Point–Line Geometric Representation Framework for Visual–Inertial Localization of UAVs and UGVs in Complex Environments

Accurate visual–inertial localization is essential for unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) operating in challenging environments. However, conventional multi-state constraint Kalman filter (MSCKF)-based visual–inertial odometry (VIO) uses landmark-based measurements with null-space projection and often delays visual updates until feature tracks end, while point-based systems may provide insufficient geometric constraints under low texture, motion blur, and illumination variations. To overcome these limitations, we extend our preliminary conference work, POPL-VIO, into a complete state estimation framework built upon pose-only (PO) representations for both point and line features. Specifically, a unified pose-only measurement model is developed for MSCKF point and line features. Feature geometry is substituted before linearization, avoiding independent landmark Jacobians and null-space projection while enabling state updates as soon as three valid views are available. Long-track SLAM points and lines are retained in the hybrid filter state and updated using landmark-based measurements. To improve the quality of geometric constraints, the unified base-frame selection algorithm of POPL-VIO is refined and analyzed for both feature types. Additionally, to ensure observational reliability in challenging environments, a stringent outlier rejection mechanism integrating image grid segmentation and bidirectional optical flow consistency is proposed. An observability analysis shows that the PO point and line measurements preserve the four fundamental unobservable directions of visual–inertial navigation, both in the linearized system and, under compatible first-estimate Jacobians, in the filter. The method is evaluated on the EuRoC MAV and KAIST Urban datasets, as well as self-collected indoor sequences acquired by manually carrying a UAV-configured sensor suite to produce UAV-like sensor motion and separate ground-platform sequences. The proposed method achieves the lowest mean ATE among the evaluated methods on EuRoC and KAIST, with mean errors of 0.103 m and 9.07 m, respectively, and also improves the mean results on the self-collected sequences. On the EuRoC MH_01 sequence, it achieves an average processing rate of approximately 35 Hz on the tested PC. These results indicate that the proposed pose-only formulation improves localization accuracy while maintaining processing efficiency comparable to that of the corresponding point-line system without the pose-only formulation.