Undelayed Initialization of Inverse Depth Parameterized Landmarks in UKF-SLAM with Error State Formulation

In this paper we present an approach to combine error state estimation with total state monocular simultaneous localization and mapping (SLAM) in a single Unscented Kalman Filter (UKF). The map features use the inverse depth parametrization for undelayed initialization and for the ability to use low-parallax features with unknown depth information. Furthermore, a new map feature initialization method is presented using the Unscented transform (UT). This method allows to capture all correlations between the map features and the error state variables without the necessity to calculate any Jacobian matrices.

[1]  Javier Civera,et al.  Unified Inverse Depth Parametrization for Monocular SLAM , 2006, Robotics: Science and Systems.

[2]  Stergios I. Roumeliotis,et al.  On the complexity and consistency of UKF-based SLAM , 2009, 2009 IEEE International Conference on Robotics and Automation.

[3]  Axel Barrau,et al.  Invariant filtering for Pose EKF-SLAM aided by an IMU , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[4]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[5]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[6]  Stergios I. Roumeliotis,et al.  A unified framework for nearby and distant landmarks in bearing-only SLAM , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[7]  Michel Devy,et al.  Undelayed initialization in bearing only SLAM , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[9]  Enrico Cestino,et al.  Design of a High-Altitude Long-Endurance Solar-Powered Unmanned Air Vehicle for Multi-Payload and Operations , 2007 .

[10]  Roland Siegwart,et al.  Real-time metric state estimation for modular vision-inertial systems , 2011, 2011 IEEE International Conference on Robotics and Automation.

[11]  Andrew J. Davison,et al.  Real-time simultaneous localisation and mapping with a single camera , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[12]  Yuanxin Wu,et al.  Unscented Kalman filtering for additive noise case: augmented versus nonaugmented , 2005, IEEE Signal Processing Letters.

[13]  Javier Civera,et al.  Inverse Depth Parametrization for Monocular SLAM , 2008, IEEE Transactions on Robotics.

[14]  Franz Andert,et al.  Lidar-Aided Camera Feature Tracking and Visual SLAM for Spacecraft Low-Orbit Navigation and Planetary Landing , 2015 .

[15]  Franz Andert,et al.  Visual navigation for autonomous, precise and safe landing on celestial bodies using unscented Kalman filtering , 2017, 2017 IEEE Aerospace Conference.

[16]  Tom Drummond,et al.  Scalable Monocular SLAM , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[17]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[18]  Chieh-Chih Wang,et al.  Achieving undelayed initialization in monocular SLAM with generalized objects using velocity estimate-based classification , 2011, 2011 IEEE International Conference on Robotics and Automation.

[19]  S. Theil,et al.  ATON (Autonomous Terrain-based Optical Navigation) for exploration missions: recent flight test results , 2018 .

[20]  Jiyoung Park,et al.  Monocular SLAM with undelayed initialization for an indoor robot , 2012, Robotics Auton. Syst..