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Camera Motion Estimation from RGB-D-Inertial Scene Flow (2404.17251v1)

Published 26 Apr 2024 in cs.CV

Abstract: In this paper, we introduce a novel formulation for camera motion estimation that integrates RGB-D images and inertial data through scene flow. Our goal is to accurately estimate the camera motion in a rigid 3D environment, along with the state of the inertial measurement unit (IMU). Our proposed method offers the flexibility to operate as a multi-frame optimization or to marginalize older data, thus effectively utilizing past measurements. To assess the performance of our method, we conducted evaluations using both synthetic data from the ICL-NUIM dataset and real data sequences from the OpenLORIS-Scene dataset. Our results show that the fusion of these two sensors enhances the accuracy of camera motion estimation when compared to using only visual data.

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References (34)
  1. Fast ego-motion estimation with multi-rate fusion of inertial and vision. The International Journal of Robotics Research, 26(6):577–589, 2007.
  2. The computation of optical flow. ACM Computing Surveys, 27(3):433–466, 1995.
  3. Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam. IEEE Transactions on Robotics, 37(6):1874–1890, 2021.
  4. On-Manifold Preintegration for Real-Time Visual–Inertial Odometry. IEEE Transactions on Robotics, 33(1):1–21, 2017.
  5. A benchmark for rgb-d visual odometry, 3d reconstruction and slam. In 2014 IEEE International Conference on Robotics and Automation (ICRA), pages 1524–1531, 2014.
  6. Integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds. Information Fusion, 14(1):57–77, 2013.
  7. Fast visual odometry for 3-d range sensors. IEEE Transactions on Robotics, 31(4):809–822, 2015.
  8. A primal-dual framework for real-time dense rgb-d scene flow. In 2015 IEEE international conference on robotics and automation (ICRA), pages 98–104. IEEE, 2015.
  9. Robust planar odometry based on symmetric range flow and multiscan alignment. IEEE Transactions on Robotics, 34(6):1623–1635, 2018.
  10. Robust odometry estimation for RGB-D cameras. In 2013 IEEE International Conference on Robotics and Automation, pages 3748–3754, Karlsruhe, Germany, 2013. IEEE.
  11. Dense continuous-time tracking and mapping with rolling shutter rgb-d cameras. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 2264–2272, 2015.
  12. Dense rgb-d-inertial slam with map deformations. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6741–6748, 2017a.
  13. Dense RGB-D-inertial SLAM with map deformations. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6741–6748, Vancouver, BC, 2017b. IEEE.
  14. Aggressive perception-aware navigation using deep optical flow dynamics and pixelmpc. IEEE Robotics and Automation Letters, 5(2):1207–1214, 2020.
  15. Keyframe-based visual–inertial odometry using nonlinear optimization. The International Journal of Robotics Research, 34(3):314–334, 2015.
  16. RGBD Scene Flow Estimation with Global Nonrigid and Local Rigid Assumption. Discrete Dynamics in Nature and Society, 2020:1–9, 2020.
  17. Combining inertial navigation and icp for real-time 3d surface reconstruction. In Eurographics (Short Papers), pages 13–16. Citeseer, 2014.
  18. Maximum likelihood identification of inertial sensor noise model parameters. IEEE Sensors Journal, 16(1):163–176, 2016.
  19. Visual-inertial teach and repeat. Robotics and Autonomous Systems, 131:103577, 2020.
  20. S-ptam: Stereo parallel tracking and mapping. Robotics and Autonomous Systems, 93:27–42, 2017.
  21. Learning multi-object tracking and segmentation from automatic annotations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6846–6855, 2020.
  22. Learning object class detectors from weakly annotated video. In 2012 IEEE Conference on computer vision and pattern recognition, pages 3282–3289. IEEE, 2012.
  23. Dense Semi-rigid Scene Flow Estimation from RGBD Images. In Computer Vision – ECCV 2014, pages 567–582. Springer International Publishing, Cham, 2014. Series Title: Lecture Notes in Computer Science.
  24. Nerf-slam: Real-time dense monocular slam with neural radiance fields. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023.
  25. Neuromorphic optical flow and real-time implementation with event cameras. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4128–4137, 2023.
  26. Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping. In 2020 IEEE/RSJ international conference on intelligent robots and systems (IROS), pages 5135–5142. IEEE, 2020.
  27. RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots. Sensors, 19(10):2251, 2019.
  28. Are we ready for service robots? the OpenLORIS-Scene datasets for lifelong SLAM. In 2020 International Conference on Robotics and Automation (ICRA), pages 3139–3145, 2020.
  29. Range flow estimation. Comput. Vis. Image Underst., 85(3):209–231, 2002.
  30. Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras. Advances in neural information processing systems, 34:16558–16569, 2021.
  31. Three-dimensional scene flow. In Proceedings of the Seventh IEEE International Conference on Computer Vision, pages 722–729 vol.2, Kerkyra, Greece, 1999. IEEE.
  32. Optical flow and scene flow estimation: A survey. Pattern Recognition, 114:107861, 2021.
  33. Flowfusion: Dynamic dense rgb-d slam based on optical flow. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 7322–7328. IEEE, 2020.
  34. Unsupervised event-based optical flow using motion compensation. In Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018.

Summary

  • The paper introduces a novel RGB-D-inertial fusion method that optimizes camera motion estimation using multi-frame and marginalization techniques.
  • The approach leverages direct scene flow from RGB-D data integrated with IMU readings to significantly reduce positional errors.
  • Experimental evaluations on synthetic and real datasets demonstrate improved precision and efficiency compared to traditional single-modality methods.

Enhanced Camera Motion Estimation Through RGB-D-Inertial Integration

Introduction

The integration of sensory data for autonomous navigation has distinct advantages in terms of accuracy and robustness. This paper introduces a novel RGB-D-inertial formulation for estimating camera motion in rigid environments. The authors propose a tightly coupled optimization strategy that spans multi-frame scenarios to effectively utilize RGB-D and Inertial Measurement Unit (IMU) data. They introduce a methodological advancement by fusing inertial readings with visual information directly derived from scene flow without prior feature extraction.

Contributions and Methodology

The paper's primary contribution is the fusion method that incorporates inertial data with RGB-D to estimate camera motion. This approach shows marked improvements in the precision of motion estimates under the following setups:

  • Multi-Frame Optimization: Leveraging multi-frame data to enhance motion estimation accuracy.
  • Marginalization Strategy: Employing a strategic marginalization of older states to refine current predictions and sustain computational efficiency.

The method integrates camera pose estimation through scene flow, emphasizing direct measurement utilization. This approach differentiates itself from prior works which commonly separate feature extraction from inertial integration.

Technical Approach

The technical approach is divided into several parts:

  • Scene Flow Estimation: Utilizing RGB-D data to calculate the three-dimensional motion field of scene points.
  • IMU Data Integration: Incorporating accelerometer and gyroscope readings to enhance the motion estimation, accounting for device bias and noise.
  • Joint Optimization: Deploying a cost function that embeds both the visual residuals (from scene flow) and inertial residuals, balanced by their respective covariances.
  • Marginalization: Applying a marginalization process to older states to retain a manageable computation load without significant information loss.

Evaluation

Evaluations were performed on synthetic data from the ICL-NUIM dataset and on real-world data from the OpenLORIS-Scene dataset. Results were compared against standard RGB-D based methods. The proposed method significantly reduced positional error metrics and showed more consistent performance in realistic dynamic settings. Notably, the improvements in error metrics were pronounced when integrating inertial data, which substantiates the benefit of multi-sensory fusion over single-modality systems. Furthermore, the application of state marginalization showcased an ability to maintain or slightly improve the accuracy of the system while managing computational demands effectively.

Theoretical Implications

From a theoretical standpoint, this paper advances understanding of how integration techniques can leverage the complementary nature of RGB-D and inertial data. The formulation highlights the effectiveness of direct optimization methods in the context of motion estimation, setting a foundation for future explorations into more complex or varied environmental interactions.

Speculations on Future AI Developments

Looking forward, the fusion of RGB-D and inertial data could see broader applications in areas requiring high precision navigation under dynamic conditions, such as in autonomous vehicles and robotics in populous environments. Moreover, enhancements in sensor technology or algorithmic efficiency could enable real-time applications in more computationally constrained platforms such as mobile devices and drones. Potential research could also explore the resilience of these methods in adversarial conditions or their adaptation to underwater or aerial navigation scenarios where traditional sensors face limitations.

In conclusion, the proposed RGB-D-inertial integration approach represents a notable advance in sensor-based odometry. It offers a novel perspective on leveraging the intrinsic strengths of each sensor modality to enhance motion estimation accuracy effectively.