鲁棒的非线性优化的立体视觉－惯导SLAM

针对基于视觉特征的同时定位与地图构建（SLAM）系统在图像模糊、运动过快和特征缺失的情况下存在鲁棒性和精度急剧下降甚至失败的问题，提出了紧耦合的非线性优化的立体视觉-惯导SLAM系统．首先，以关键帧的位姿作为约束，采用分而治之的策略估计惯性测量单元（IMU）的偏差．在前端，针对ORB-SLAM2在跟踪过程中由于运动过快导致匀速运动模型失效的问题，通过预积分上一帧到当前帧的IMU数据，预测当前帧的初始位姿，并在位姿优化中加入了IMU预积分约束．然后，在后端优化中，在滑动窗口内优化关键帧的位姿、地图点和IMU预积分，并更新IMU的偏差．最后，通过EuRoC数据集验证该系统的性能，对比ORB-SLAM2系统、VINS-Mono系统和OKVIS系统，该系统的精度分别提高了1.14倍、1.48倍和4.59倍；相比前沿的SLAM系统，该系统在快速运动、图像模糊和特征缺失条件下的鲁棒性也得到了提高．

Robust Stereo Visual-Inertial SLAM Using Nonlinear Optimization

In the case of image blurring, excessive motion and lack of features, the robustness and accuracy of feature-based simultaneous localization and mapping (SLAM) system decline dramatically or even fail. For this problem, a tightly coupled stereo visual-inertial SLAM system using nonlinear optimization is proposed. Firstly, taking the pose of the keyframes as a constraint, the biases of the inertial measurement unit (IMU) are estimated using the divide and conquer strategy. In the front-end, in order to solve the problem that ORB-SLAM2 fails to use the constant velocity motion model due to excessive motion during the tracking process, the initial pose of the current frame is predicted by pre-integrating the IMU data from the previous frame to the current frame, and IMU pre-integration constraints are added to pose optimization. Then, in the back-end optimization, keyframe poses, map points, and IMU pre-integration are optimized within a sliding window and IMU deviations are updated. Finally, the performance of the system is verified with the EuRoC dataset. Comparing with the ORB-SLAM2 system, VINS-Mono system and OKVIS system, the accuracy of the proposed system is improved by 1.14, 1.48 and 4.59 times, respectively. Comparing with the state-of-the-art SLAM systems, the robustness of the system is improved under the conditions of excessive motion, image blurring, and lack of features.