Visual-Inertial Odometry of Aerial Robots

Visual-Inertial odometry (VIO) is the process of estimating the state (pose and velocity) of an agent (e.g., an aerial robot) by using only the input of one or more cameras plus one or more Inertial Measurement Units (IMUs) attached to it. VIO is the only viable alternative to GPS and lidar-based odometry to achieve accurate state estimation. Since both cameras and IMUs are very cheap, these sensor types are ubiquitous in all today's aerial robots.

[1]  F. W. Cathey,et al.  The iterated Kalman filter update as a Gauss-Newton method , 1993, IEEE Trans. Autom. Control..

[2]  Stergios I. Roumeliotis,et al.  A First-Estimates Jacobian EKF for Improving SLAM Consistency , 2009, ISER.

[3]  Anelia Angelova,et al.  Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[5]  Shaojie Shen,et al.  Online Temporal Calibration for Monocular Visual-Inertial Systems , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[6]  Dimitrios G. Kottas,et al.  On the Consistency of Vision-Aided Inertial Navigation , 2012, ISER.

[7]  Gaurav S. Sukhatme,et al.  Sliding window filter with application to planetary landing , 2010, J. Field Robotics.

[8]  Olivier Stasse,et al.  MonoSLAM: Real-Time Single Camera SLAM , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Roland Siegwart,et al.  Extending kalibr: Calibrating the extrinsics of multiple IMUs and of individual axes , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[10]  Frank Dellaert,et al.  On-Manifold Preintegration for Real-Time Visual--Inertial Odometry , 2015, IEEE Transactions on Robotics.

[11]  Frank Dellaert,et al.  iSAM: Incremental Smoothing and Mapping , 2008, IEEE Transactions on Robotics.

[12]  Michael Gassner,et al.  SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems , 2017, IEEE Transactions on Robotics.

[13]  Stergios I. Roumeliotis,et al.  C-KLAM: Constrained keyframe-based localization and mapping , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[14]  Salah Sukkarieh,et al.  Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions , 2012, IEEE Transactions on Robotics.

[15]  Gabe Sibley,et al.  Sliding window filter with application to planetary landing , 2010 .

[16]  Frank Dellaert,et al.  iSAM2: Incremental smoothing and mapping using the Bayes tree , 2012, Int. J. Robotics Res..

[17]  Laurent Kneip,et al.  Collaborative monocular SLAM with multiple Micro Aerial Vehicles , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Stergios I. Roumeliotis,et al.  An observability-constrained sliding window filter for SLAM , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Stefano Soatto,et al.  Robust inference for visual-inertial sensor fusion , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[20]  Roland Siegwart,et al.  The EuRoC micro aerial vehicle datasets , 2016, Int. J. Robotics Res..

[21]  Gabe Sibley,et al.  A Spline-Based Trajectory Representation for Sensor Fusion and Rolling Shutter Cameras , 2015, International Journal of Computer Vision.

[22]  Roland Siegwart,et al.  Keyframe-based Visual-Inertial SLAM using Nonlinear Optimization , 2013, RSS 2013.

[23]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Agostino Martinelli,et al.  Observability Properties and Deterministic Algorithms in Visual-Inertial Structure from Motion , 2013, Found. Trends Robotics.

[25]  Guoquan Huang,et al.  Visual-Inertial Navigation: A Concise Review , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[26]  Daniel Matolin,et al.  A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS , 2011, IEEE Journal of Solid-State Circuits.

[27]  Marc Pollefeys,et al.  PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision , 2012, Auton. Robots.

[28]  Frank Dellaert,et al.  Information fusion in navigation systems via factor graph based incremental smoothing , 2013, Robotics Auton. Syst..

[29]  Davide Scaramuzza,et al.  SVO: Fast semi-direct monocular visual odometry , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[30]  Roland Siegwart,et al.  Vision-Controlled Micro Flying Robots: From System Design to Autonomous Navigation and Mapping in GPS-Denied Environments , 2014, IEEE Robotics & Automation Magazine.

[31]  Roland Siegwart,et al.  A robust and modular multi-sensor fusion approach applied to MAV navigation , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[32]  Davide Scaramuzza,et al.  A Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[33]  F. Dellaert.  Factor Graphs and GTSAM: A Hands-on Introduction , 2012 .

[34]  Stefan Leutenegger,et al.  Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera , 2016, ECCV.

[35]  Stefano Soatto,et al.  Visual-inertial navigation, mapping and localization: A scalable real-time causal approach , 2011, Int. J. Robotics Res..

[36]  Roland Siegwart,et al.  Unified temporal and spatial calibration for multi-sensor systems , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[37]  Gaurav S. Sukhatme,et al.  Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration , 2011, Int. J. Robotics Res..

[38]  Camillo J. Taylor,et al.  Camera trajectory estimation using inertial sensor measurements and structure from motion results , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[39]  Tobi Delbrück,et al.  A 128$\times$ 128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor , 2008, IEEE Journal of Solid-State Circuits.

[40]  Stergios I. Roumeliotis,et al.  A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[41]  David W. Murray,et al.  Parallel Tracking and Mapping on a camera phone , 2009, 2009 8th IEEE International Symposium on Mixed and Augmented Reality.

[42]  Sanjiv Singh,et al.  Motion Estimation from Image and Inertial Measurements , 2004, Int. J. Robotics Res..

[43]  T. Delbruck,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < 1 , 2022 .

[44]  Roland Siegwart,et al.  Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization , 2013, Robotics: Science and Systems.

[45]  Shaojie Shen,et al.  VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator , 2017, IEEE Transactions on Robotics.

[46]  Kostas Daniilidis,et al.  Event-Based Visual Inertial Odometry , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[47]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[48]  Roland Siegwart,et al.  Vision-based MAV Navigation: Implementation Challenges Towards a Usable System in Real-Life Scenarios , 2012, RSS 2012.

[49]  Roland Siegwart,et al.  Robust visual inertial odometry using a direct EKF-based approach , 2015, IROS 2015.

[50]  Peter S. Maybeck,et al.  Stochastic Models, Estimation And Control , 2012 .

[51]  Peter Corke,et al.  An Introduction to Inertial and Visual Sensing , 2007, Int. J. Robotics Res..

[52]  Sen Wang,et al.  DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[53]  Stergios I. Roumeliotis,et al.  A dual-layer estimator architecture for long-term localization , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[54]  Gaurav S. Sukhatme,et al.  A General Framework for Temporal Calibration of Multiple Proprioceptive and Exteroceptive Sensors , 2010, ISER.

[55]  Roland Siegwart,et al.  Monocular Vision for Long‐term Micro Aerial Vehicle State Estimation: A Compendium , 2013, J. Field Robotics.

[56]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[57]  Ryad Benosman,et al.  Simultaneous Mosaicing and Tracking with an Event Camera , 2014, BMVC.

[58]  Davide Scaramuzza,et al.  EVO: A Geometric Approach to Event-Based 6-DOF Parallel Tracking and Mapping in Real Time , 2017, IEEE Robotics and Automation Letters.

[59]  Davide Scaramuzza,et al.  Event-Based, 6-DOF Camera Tracking from Photometric Depth Maps , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[60]  Anastasios I. Mourikis,et al.  3-D motion estimation and online temporal calibration for camera-IMU systems , 2013, 2013 IEEE International Conference on Robotics and Automation.

[61]  Hauke Strasdat,et al.  Real-time monocular SLAM: Why filter? , 2010, 2010 IEEE International Conference on Robotics and Automation.

[62]  Davide Scaramuzza,et al.  Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High-Speed Scenarios , 2017, IEEE Robotics and Automation Letters.

[63]  Davide Scaramuzza,et al.  EMVS: Event-Based Multi-View Stereo—3D Reconstruction with an Event Camera in Real-Time , 2017, International Journal of Computer Vision.

[64]  Noah Snavely,et al.  Unsupervised Learning of Depth and Ego-Motion from Video , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).