ABSTRACT
Random sample consensus (RANSAC) method is often utilized in the RGB-D Simultaneous localization and mapping (SLAM) systems and it is time-consuming because of more repeated fitting the transformation matrix. This paper aims to find a feature point matching method that can reduce computation time in the RGB-D SLAM system. We explore an approach based on four-point order-preserving constraint to determine inliers between two adjacent images. Firstly, the four-point order-preserving constraint between two frames is established to find the good inliers. Then, the 3D points corresponding to the good inliers are obtained to compute the transformation matrix in SLAM system. Finally, the localization and mapping in SLAM system are implemented from transformation matrix and the Global Graph Optimization (g2o) framework. The results indicate that our method is faster and more accurate than the RANSAC algorithm. The less computational time is significant for the real-time SLAM system, and the proposed method is clearly helpful for that.
- F. Endres, J. Hess, J. Sturm, et al (2014). 3-D mapping with an RGB-D camera, IEEE Transactions on Robotics, 30(1), 177--187.Google ScholarDigital Library
- A. R. Khairuddin, M. S. Talib, H. Haron (2015). Review on simultaneous localization and mapping (SLAM), IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, November 85-90.Google ScholarCross Ref
- D. Droeschel, S. Behnke (2018). Efficient continuous-time SLAM for 3D lider-based online mapping, Proceedings - IEEE International Conference on Robotics and Automation, 5000--5007.Google Scholar
- J. Castorena (2019). Computational mapping of the ground reflectivity with laser scanners, IEEE Transactions on Image Processing, 28(9), 4288--4298.Google ScholarCross Ref
- G. Xing, Y. Liu, H. Ling, X. Granier, Y, Zhang (2020). Automatic spatially varying illumination recovery of indoor scenes based on a single RGB-D image, IEEE Transactions on visualization and computer graphics, 26(4), 1672--1685.Google Scholar
- Y. Guo, F. Deligianni, X. Gu, G.Z.Yang (2019). 3D canonical pose estimation and abnormal gait recognition with a single RGB-D camera, IEEE Robotics and Automation letters, 4(4), 3617--3624.Google Scholar
- S. Suresh, E. Westman, M.Kaess (2019). Through-water stereo SLAM with refraction correction for AUV localization, IEEE Robotics and Automation Letters, 4(2), 692--699.Google ScholarCross Ref
- H. Matsuki, L. Von Stumberg, V. Usenko, et.al (2018). Ominidirectional DSO: Direct sparse odometry with fisheye camera, IEEE Robotics and Automation Letters, 3(4), 3693--3700.Google ScholarCross Ref
- R. A. Newcombe, S. Izadi, O. Hilliges, et al (2012). KinectFusion: Real-time dense surface mapping and tracking, IEEE International Symposium on Mixed and Augmented Reality, Atlanta, USA, 127--136.Google Scholar
- P. Henry, M. Krainin, E. Herbst, et al (2012). RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments, International Journal of Robotics Research, 31(5), 647--663.Google ScholarDigital Library
- F. Endres, J. Hess, N. Engelhard, et al (2012). An evaluation of the RGB-D SLAM system, IEEE International Conference on Robotics and Automation (ICRA), St. Paul, USA, 1691--1696.Google ScholarCross Ref
- C. K. Hao, N. M. Mayer (2013). Real-time SLAM using an RGB-D camera for mobile robots, IEEE International Automatic Control Conference (CACS), Nantou, Taiwan, 356--361.Google ScholarCross Ref
- H. P. Quang, N. L. Quoc (2015). Some improvements in the RGB-D SLAM system, 2015 IEEE RIVF International Conference on Computing & Communication Technologies-Research, Innovation, and Vision for the Future (RIVF), CanTho, Vietnam, 112--116.Google Scholar
- R. Kümmerle, G. Grisetti, H. Strasdat, et al (2011). g2o: A general framework for graph optimization, IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 3607--3613.Google Scholar
- H. Durrant-Whyte, T. Bailey (2006). Simultaneous localization and mapping: part I, IEEE robotics & automation magazine, 13(2), 99--110.Google Scholar
- T. Bailey, H. Durrant-Whyte (2006). Simultaneous localization and mapping (SLAM): Part II, IEEE Robotics & Automation Magazine, 13(3), 108--117.Google ScholarCross Ref
- D. G. Lowe (2004). Distinctive image features from scale-invariant key points, International journal of computer vision, 60(2), 91--110.Google Scholar
- H. Bay, A. Ess, T. Tuytelaars, et al (2008). Speeded-up robust features (SURF), Computer vision and image understanding, 110(3), 346--359.Google Scholar
- E. Rublee, V. Rabaud, K. Konolige, et al (2011). ORB: An efficient alternative to SIFT or SURF, IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, 2564--2571.Google ScholarDigital Library
- J. Guo, D. Hermelin, C. Komusiewicz (2014). Local Search for String Problems: Brute Force Is Essentially Optimal, Theoretical Computer Science, 525(4), 30--41.Google ScholarDigital Library
- M. A. Fischler, R. C. Bolles (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, 24(6), 381--395.Google ScholarDigital Library
- P. Márquez-Neila, J. López-Alberca, J. M. Buenaposada, et al (2016). Speeding-up homography estimation in mobile devices, Journal of Real-Time Image Processing, 11(1), 141--154.Google ScholarDigital Library
- V. Ferrari, T. Tuytelaars, L. V. Gool (2003). Wide-baseline multiple-view correspondences, IEEE Conference on Computer Vision and Pattern Recognition, Madison, USA, I-I.Google ScholarCross Ref
- http://cs.nyu.edu/~silberman/datasets/Google Scholar
- J. Sturm, N. Engelhard, F. Endres, et al (2012). A benchmark for the evaluation of RGB-D SLAM systems, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal, 573--580.Google ScholarCross Ref
- S. Rosa, S. Toscana, B. Bona (2017). Q-PSO: Fast Quaternion-Based Pose Estimation from RGB-D Images, Journal of Intelligent and Robotic Systems: Theory and Applications, 1--23.Google Scholar
- Hartley R.I., Zisserman A.2004. Multiple View Geometry in Computer Vision (2nd. ed).Cambridge University Press, Cambridge, Chapter 4.Google Scholar
- http://opencv.willowgarage.comGoogle Scholar
Index Terms
- Feature Point Matching Based on Four-point Order Consistency in the RGB-D SLAM System
Recommendations
RGB-D SLAM with Deep Depth Completion
Artificial Intelligence and Soft ComputingAbstractRGB-D indoor mapping has been an active research topic in the last decade with the release of various depth sensors. Researchers proposed impressive SLAM systems such as ORB-SLAM2. However, the depth sensors are sensitive to illumination ...
An Indoor RGB-D Dataset for the Evaluation of Robot Navigation Algorithms
ACIVS 2013: 15th International Conference on Advanced Concepts for Intelligent Vision Systems - Volume 8192The paper presents a RGB-D dataset for development and evaluation of mobile robot navigation systems. The dataset was registered using a WiFiBot robot equipped with a Kinect sensor. Unlike the presently available datasets, the environment was ...
Improving RGB-D SLAM using wi-fi: poster abstract
IPSN '17: Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor NetworksSimultaneous Localization and Mapping (SLAM) is the process of learning about both the environment and about a robot's location with respect to the environment and is essential for robots to autonomously navigate. A variety of algorithms using many ...
Comments