Lidar-based simultaneous localization and mapping(SLAM) algorithms rely on the structural features to estimate the poses and construct the environmental map. Therefore, the pose estimation accuracy and robustness of such algorithms will decline or even fail in structureless environments. To solve this problem, combined with the characteristics that an inertial measurement unit(IMU) is not constrained by the environment and the camera depends on visual texture, a stereo vision aided lidar-inertial SLAM algorithm is proposed to solve the degradation problem of a pure lidar SLAM algorithm in structureless environments. This paper uses a stereo visual-inertial odometry algorithm to provide a priori pose for the lidar scan matching module, and further combines the visual pose constraint with the lidar structure feature constraints for joint pose estimation. In addition, a combined strategy of a complementary filtering algorithm and a factor graph optimization algorithm is proposed to align the lidar odometry reference frame with the inertial frame. Based on factor graph optimization, the lidar pose and IMU data are fused to constrain the IMU bias, so as to provide an alternative relative pose prediction for lidar scanning matching in the case of visual odometey failure. In addition, in order to further improve the accuracy of global trajectory estimation, this paper proposes a hybrid loop-closure detection strategy based on an iterative closest point matching algorithm(ICP) and an image feature matching algorithm, and the six degree of freedom pose graph optimization method is used to significantly reduce the odometey drift error and construct a global consistency environment map. Finally, experiments are carried out on public and self-made data sets, and the proposed method is compared with the mainstream open source SLAM algorithms. The experimental results show that the proposed algorithm can work stably in structureless environments, and achieve higher accuracy and robustness than the comparison algorithm.