Drift-correcting self-calibration for visual-inertial SLAM
Drift-correcting self-calibration for visual-inertial SLAM
复制标题
视觉惯性 SLAM 的漂移校正自校准
DOI：
10.1109/icra.2017.7989771
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
C. Heckman
中科院分区：
文献类型：
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作者：
Fernando Nobre;Mike Kasper;C. Heckman
提出了一种在校准参数存在漂移的情况下进行在线同步定位与测绘（SLAM）自校准的解决方案，以支持精确的长期运行。校准参数，如相机焦距或相机到imu的外部结构，在长时间的操作中经常发生漂移，从而在重建中引起累积误差。关键的贡献是将校准参数建模为一个时空量：传感器到传感器的空间校准和传感器固有参数是连续时变的，并通过统计测试进行变化检测和回归。对时变传感器标定建模不当的长期影响进行了分析。通过只选择固定数量的轨迹信息段进行校准参数估计来实现恒定时间操作，通过不将过去的测量滚动到先验分布中，从而避免早期线性化误差。我们的方法通过模拟和现实世界的数据进行了验证。
We present a solution for online simultaneous localization and mapping (SLAM) self-calibration in the presence of drift in calibration parameters in order to support accurate long-term operation. Calibration parameters such as the camera focal length or camera-to-IMU extrinsics are frequently subject to drift over long periods of operation, inducing cumulative error in the reconstruction. The key contributions are modeling calibration parameters as a spatiotemporal quantity: sensor-to-sensor spatial calibration and sensor intrinsic parameters are continuously time-varying, with statistical tests for change detection and regression. An analysis of the long term effects of inappropriately modeling time-varying sensor calibration is also provided. Constant-time operation is achieved by selecting only a fixed number of informative segments of the trajectory for calibration parameter estimation, giving the added benefit of avoiding early linearization errors by not rolling past measurements into a prior distribution. Our approach is validated with simulated and real-world data.