鲁棒的非线性优化的立体视觉－惯导SLAM

doi:10.13973/j.cnki.robot.170673

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林辉灿, 吕强, 王国胜, 卫恒, 梁冰. 鲁棒的非线性优化的立体视觉－惯导SLAM[J]. 机器人, 2018, 40(6): 911-920.DOI: 10.13973/j.cnki.robot.170673. LIN Huican, LÜ Qiang, WANG Guosheng, WEI Heng, LIANG Bing. Robust Stereo Visual-Inertial SLAM Using Nonlinear Optimization. ROBOT, 2018, 40(6): 911-920. DOI: 10.13973/j.cnki.robot.170673.

摘要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．

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关键词 ：计算机视觉, 同时定位与地图构建, 传感器融合, 视觉-惯导系统, 紧耦合, 状态估计

Abstract：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.

Key words： computer vision simultaneous localization and mapping (SLAM) sensor fusion visual-inertial system tightly coupled state estimation

收稿日期: 2017-12-14 录用日期: 2018-04-02

TP242.6

基金资助:国家自然科学基金（61663014）

通讯作者: 吕强,rokyou@live.cn E-mail: rokyou@live.cn

作者简介: 林辉灿(1989-),男,博士生.研究领域:计算机视觉,VSLAM,自主机器人
吕强(1962-),男,博士,教授.研究领域:计算机视觉,深度学习,机器人控制
王国胜(1975-),男,博士,副教授.研究领域:非线性控制.

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https://robot.sia.cn/CN/10.13973/j.cnki.robot.170673 或 https://robot.sia.cn/CN/Y2018/V40/I6/911

[1] Zhou Q Q, Lei S Y, Yu Z G, et al. Indoor positioning system using axis alignment and complementary IMUs for robot localization[J]. Robot, 2017, 39(3):316-323.
[2] Martinelli A. Vision and IMU data fusion:Closed-form solutions for attitude, speed, absolute scale, and bias determination[J]. IEEE Transactions on Robotics, 2012, 28(1):44-60.
[3] Lin Y, Gao F, Qin T, et al. Autonomous aerial navigation using monocular visual-inertial fusion[J]. Journal of Field Robotics, 2017, 35(1):23-51.
[4] Li P L, Qin T, Hu B T, et al. Monocular visual-inertial state estimation for mobile augmented reality[C]//IEEE International Symposium on Mixed and Augmented Reality. Piscataway, USA:IEEE, 2017:11-21.
[5] Weiss S, Achtelik M W, Lynen S, et al. Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2012:957-964.
[6] Lynen S, Achtelik M W, Weiss S, et al. A robust and modular multi-sensor fusion approach applied to MAV navigation[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2013:3923-3929.
[7] 周绍磊,吴修振,刘刚,等:2016} 周绍磊,吴修振,刘刚,等. 一种单目视觉ORB-SLAM/INS组合导航方法[J]. 中国惯性技术学报,2016,24(5):633-637. Zhou S L, Wu X Z, Liu G, et al. Integrated navigation method of monocular ORB-SLAM/INS[J]. Journal of Chinese Inertial Technology, 2016, 24(5):633-637.
[8] Mur-Artal R, Montiel J M M, Tardos J D. ORB-SLAM:A versatile and accurate monocular SLAM system[J]. IEEE Transactions on Robotics, 2015, 31(5):1147-1163.
[9] Concha A, Loianno G, Kumar V, et al. Visual-inertial direct SLAM[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2016:1331-1338.
[10] Mur-Artal R, Tardós J D. Visual-inertial monocular SLAM with map reuse[J]. IEEE Robotics and Automation Letters, 2017, 2(2):796-803.
[11] Paul M K, Wu K, Hesch J A, et al. A comparative analysis of tightly-coupled monocular, binocular, and stereo VINS[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2017:165-172.
[12] Leutenegger S, Lynen S, Bosse M, et al. Keyframe-based visualinertial odometry using nonlinear optimization[J]. International Journal of Robotics Research, 2015, 34(3):314-334.
[13] Usenko V, Engel J, Stückler J, et al. Direct visual-inertial odom-etry with stereo cameras[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2016:1885-1892.
[14] Huai J Z, Toth C K, Grejner-Brzezinska D A. Stereo-inertial odometry using nonlinear optimization[C]//28th International Technical Meeting of the Satellite Division of the Institute of Navigation. Tampa, USA:Institute of Navigation, 2015:2087-2097.
[15] Lupton T, Sukkarieh S. Visual-inertial-aided navigation for high-dynamic motion in built environments without initial conditions[J]. IEEE Transactions on Robotics, 2012, 28(1):61-76.
[16] Forster C, Carlone L, Dellaert F, et al. On-manifold preintegration for real-time visual-inertial odometry[J]. IEEE Transactions on Robotics, 2017, 33(1):1-21.
[17] Tateno K, Tombari F, Laina I, et al. CNN-SLAM:Real-time dense monocular SLAM with learned depth prediction[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, USA:IEEE, 2017:6565-6574.
[18] Clark R, Wang S, Wen H, et al. VINet:Visual-inertial odometry as a sequence-to-sequence learning problem[C]//AAAI Conference on Artificial Intelligence. Palo Alto, USA:AAAI, 2017:3995-4001.
[19] Mur-Artal R, Tardos J D. ORB-SLAM2:An open-source SLAM system for monocular, stereo, and RGB-D cameras[J]. IEEE Transactions on Robotics, 2017, 33(5):1255-1262.
[20] Furgale P, Rehder J, Siegwart R. Unified temporal and spatial calibration for multi-sensor systems[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2013:1280-1286.
[21] Burri M, Nikolic J, Gohl P, et al. The EuRoC micro aerial vehicle datasets[J]. International Journal of Robotics Research, 2016, 35(10):1157-1163.
[22] Sturm J, Engelhard N, Endres F, et al. A benchmark for the evaluation of RGB-D SLAM systems[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2012:573-580.
[23] 赵洋,刘国良,田国会,等:2017赵洋,刘国良,田国会,等. 基于深度学习的视觉SLAM综述[J]. 机器人,2017,39(6):889-896. Zhao Y, Liu G L, Tian G H, et al. A survey of visual SLAM based on deep learning[J]. Robot, 2017, 39(6):889-896.