Robotics: Science and Systems IX

Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization

Stefan Leutenegger, Paul Furgale, Vincent Rabaud, Margarita Chli, Kurt Konolige, Roland Siegwart


The fusion of visual and inertial cues has become popular in robotics due to the complementary nature of the two sensing modalities. While most fusion strategies to date rely on filtering schemes, the visual robotics community has recently turned to non-linear optimization approaches for tasks such as visual Simultaneous Localization And Mapping (SLAM), following the discovery that this comes with significant advantages in quality of performance and computational complexity. Following this trend, we present a novel approach to tightly integrate visual measurements with readings from an Inertial Measurement Unit (IMU) in SLAM. An IMU error term is integrated with the landmark reprojection error in a fully probabilistic manner, resulting to a joint non-linear cost function to be optimized. Employing the powerful concept of keyframes we partially marginalize old states to maintain a bounded-sized optimization window, ensuring real-time operation. Comparing against both vision-only and loosely-coupled visual-inertial algorithms, our experiments confirm the benefits of tight fusion in terms of accuracy and robustness.



    AUTHOR    = {Stefan Leutenegger AND Paul Furgale AND Vincent Rabaud AND Margarita Chli AND Kurt Konolige AND Roland Siegwart}, 
    TITLE     = {Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2013}, 
    ADDRESS   = {Berlin, Germany}, 
    MONTH     = {June},
    DOI       = {10.15607/RSS.2013.IX.037}