On-Manifold Preintegration for Real-Time Visual-Inertial Odometry

被引:1539
作者
Forster, Christian [1 ]
Carlone, Luca [2 ]
Dellaert, Frank [3 ]
Scaramuzza, Davide [1 ]
机构
[1] Univ Zurich, Robot & Percept Grp, CH-8006 Zurich, Switzerland
[2] MIT, Lab Informat & Decis Syst, 77 Massachusetts Ave, Cambridge, MA 02139 USA
[3] Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA
基金
美国农业部; 瑞士国家科学基金会; 美国国家科学基金会;
关键词
Computer vision; sensor fusion; visual-inertial odometry (VIO); ERROR PROPAGATION; EUCLIDEAN GROUP; KALMAN FILTER; VISION; CALIBRATION; INTEGRATION; NAVIGATION; SYSTEMS; FUSION; MOTION;
D O I
10.1109/TRO.2016.2597321
中图分类号
TP24 [机器人技术];
学科分类号
140102 [集成电路设计与设计自动化];
摘要
Current approaches for visual-inertial odometry (VIO) are able to attain highly accurate state estimation via nonlinear optimization. However, real-time optimization quickly becomes infeasible as the trajectory grows over time; this problem is further emphasized by the fact that inertial measurements come at high rate, hence, leading to the fast growth of the number of variables in the optimization. In this paper, we address this issue by preintegrating inertial measurements between selected keyframes into single relative motion constraints. Our first contribution is a preintegration theory that properly addresses the manifold structure of the rotation group. We formally discuss the generative measurement model as well as the nature of the rotation noise and derive the expression for the maximum a posteriori state estimator. Our theoretical development enables the computation of all necessary Jacobians for the optimization and a posteriori bias correction in analytic form. The second contribution is to show that the preintegrated inertial measurement unit model can be seamlessly integrated into a visual-inertial pipeline under the unifying framework of factor graphs. This enables the application of incremental-smoothing algorithms and the use of a structureless model for visual measurements, which avoids optimizing over the 3-D points, further accelerating the computation. We perform an extensive evaluation of our monocular VIO pipeline on real and simulated datasets. The results confirm that our modeling effort leads to an accurate state estimation in real time, outperforming state-of-the-art approaches.
引用
收藏
页码:1 / 21
页数:21
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