双目视觉辅助的激光惯导SLAM算法

激光同步定位与地图构建(simultaneous localization and mapping, SLAM)算法在位姿估计和构建环境地图时依赖环境结构特征信息,在结构特征缺乏的场景下,此类算法的位姿估计精度与鲁棒性将下降甚至运行失败.对此,结合惯性测量单元(inertial measurement unit, IMU)不受环境约束、相机依赖视觉纹理的特点,提出一种双目视觉辅助的激光惯导SLAM算法,以解决纯激光SLAM算法在环境结构特征缺乏时的退化问题.即采用双目视觉惯导里程计算法为激光扫描匹配模块提供视觉先验位姿,并进一步兼顾视觉约束与激光结构特征约束进行联合位姿估计.此外,提出一种互补滤波算法与因子图优化求解的组合策略,完成激光里程计参考系与惯性参考系对准,并基于因子图将激光位姿与IMU数据融合以约束IMU偏置,在视觉里程计失效的情况下为激光扫描匹配提供候补的相对位姿预测.为进一步提高全局轨迹估计精度,提出基于迭代最近点匹配算法(iterative closest point, ICP)与基于图像特征匹配算法融合的混合闭环检测策略,利用6自由度位姿图优化方法显著降低里程计漂移误...

Stereo vision aided lidar-inertial SLAM

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.