An Modified Realtime Photometric Calibration Algorithm Based on ORB Features * In order to improve the accuracy of direct visual odometry and SLAM algorithm under illumination changes, we propose an modified realtime photometric calibration algorithm based on ORB features. Firstly, we use more robust ORB features as input to a optimization framework to obtain scene points correspondences. Then, we establish the reprojection pixels intensity residual equation based on photometric image formation and feature matching results of the camera. Finally, the incoming frames are corrected by the camera response function, exposure time and vignetting estimation. The proposed approach is tested with the EuRoC dataset and TUM dataset respectively, and experimental results indicate that the exposure compensation and time cost performances of the proposed method are promising.

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.