Tightly Coupled Semantic RGBD Inertial Odometry for Accurate LongTerm Localization and Mapping In this paper, we utilize semantically enhanced feature matching and visual inertial bundle adjustment to improve the robustness of odometry especially in featuresparse environments. A novel semantically enhanced feature matching algorithm is developed for robust: 1) medium and longterm tracking, and 2) loopclosing. Additionally, a semantic visual inertial bundle adjustment algorithm is introduced to robustly estimate pose in presence of ambiguous correspondences or in feature sparse environment. Our tightly coupled semantic RGBD odometry approach is demonstrated on a real world indoor dataset collected using our unmanned ground vehicle (UGV). Our approach improves traditional visual odometry relying on lowlevel geometric features like corners, points, and planes for localization and mapping. Additionally, prior approaches are limited due to their sensitivity to scene geometry and changes in light intensity. The semantic inertial odometry is especially important to significantly reduce drifts in longer intervals.

LMVISLAM: Robust LowLight Monocular VisualInertial Simultaneous Localization and Mapping Visualinertial simultaneous localization and mapping (SLAM) shows significant progress in recent years due to the complementary nature of the visual and inertial sensor but still, challenges remain for lowlight environments. Recent visualinertial SLAMs often drift or even fail in lowlight conditions due to insufficient 3D2D correspondences for bundle adjustment. To address the issue, this paper performs image preprocessing firstly with a united image enhancement method involving adaptive gamma correction and contrast limited adaptive histogram equalization, which could ameliorate the brightness and contrast of the image greatly. Moreover, we track features using optical flow for adequate point correspondences in dimlight environments, and supplement the corresponding map points continually by insert keyframe and triangulation to keep tracking. Finally, we construct a tightlycoupled nonlinear optimization model, which combines a feature reprojection error on point correspondences and IMU measurement by preintegration to constrain and compensate each other for more accurate pose estimation. We validate the performance of our algorithm on public dataset and realword experiments with a mobile robot, including dark laboratory, etc., and compare against existing stateoftheart visualinertial algorithms. Experimental results indicate our algorithm outperforms other stateoftheart SLAMs in accuracy and robustness, and works reliably well for both general and lowlight environments.