Orthogonal wall correction for visual motion estimation

A good motion model is a prerequisite for many approaches to simultaneous localization and mapping. Without an absolute reference, it is however difficult to prevent drift when estimating motion. To prevent orientation drift, our approach exploits typical features of indoor environments: Straight walls that are parallel or orthogonal to each other. Our idea is to detect walls in monocular depth measurements and to correct odometry obtained from matching successive images and from inertial measurements, such that the observed walls are aligned with the main orientation estimated from the map that is being built. The experimental results indicate that orientation drift can be prevented and orientation uncertainty can be reduced greatly when applying the proposed orthogonal wall correction. This can make the difference between reliable mapping and failure.

[1]  P. S. Maybeck,et al.  The Kalman Filter: An Introduction to Concepts , 1990, Autonomous Robot Vehicles.

[2]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Patric Jensfelt,et al.  Integrated systems for Mapping and Localization , 2002 .

[4]  Azriel Rosenfeld,et al.  Scene Labeling by Relaxation Operations , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Andrea Censi,et al.  An accurate closed-form estimate of ICP's covariance , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[6]  Duane C. Brown,et al.  Close-Range Camera Calibration , 1971 .

[7]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  John J. Leonard,et al.  Explore and return: experimental validation of real-time concurrent mapping and localization , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[9]  Horst-Michael Groß,et al.  Extraction of Orientation from Floor Structure for Odometry Correction in Mobile Robotics , 2003, DAGM-Symposium.

[10]  Sebastian Thrun,et al.  Robotic mapping: a survey , 2003 .

[11]  Roland Siegwart,et al.  Orthogonal 3D-SLAM for Indoor Environments Using Right Angle Corners , 2007, EMCR.

[12]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[13]  Kai O. Arras An Introduction to Error Propagation: Derivation, Meaning and Examples of Cy = Fx Cx Fx' , 1998 .

[14]  Sebastian Thrun,et al.  FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges , 2003, IJCAI.

[15]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[16]  Rüdiger Dillmann,et al.  Using Orthogonal Surface Directions for Autonomous 3D-Exploration of Indoor Environments , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.