A Robust Stereo Semidirect SLAM System Based on Hybrid Pyramid We propose a hybrid pyramid based approach to fuse the direct and indirect methods in visual SLAM, to allow robust localization under various situations including largebaseline motion, lowtexture environment, and various illumination changes. In our approach, we first calculate coarse interframe pose estimation by matching the feature points. Subsequently, we use both direct image alignment and a multiscale pyramid method, for refining the previous estimation to attain better precision. Furthermore, we perform online photometric calibration along with pose estimation, to reduce unmodelled errors. To evaluate our approach, we conducted various realworld experiments on both public datasets and selfcollected ones, by implementing a full SLAM system with the proposed methods. The results show that our system improves both localization accuracy and robustness by a wide margin.

A Robust Stereo Camera Localization Method with Prior LiDAR Map Constrains In complex environments, lowcost and robust localization is a challenging problem. For example, in a GPSdenied environment, LiDAR can provide accurate position information, but the cost is high. In general, visual SLAM based localization methods become unreliable when the sunlight changes greatly. Therefore, inexpensive and reliable methods are required. In this paper, we propose a stereo visual localization method based on the prior LiDAR map. Different from the conventional visual localization system, we design a novel visual optimization model by matching planar information between the LiDAR map and visual image. Bundle adjustment is built by using coplanarity constraints. To solve the optimization problem, we use a graphbased optimization algorithm and a local window optimization method. Finally, we estimate a full six degrees of freedom (DOF) pose without scale drift. To validate the efficiency, the proposed method has been tested on the KITTI dataset. The results show that our method is more robust and accurate than the stateofart ORBSLAM2.