| 研究生: |
邱柏晨 Chiou, Po-Chen |
|---|---|
| 論文名稱: |
BiFGO-KISS:基於ESKF與雙層迴圈因子圖之即時定位與建圖算法 BiFGO-KISS: Simultaneous Localization and Mapping Algorithm Based on ESKF and Bi-Layers Factor Graph |
| 指導教授: |
彭兆仲
Peng, Chao-Chung |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 航空太空工程學系 Department of Aeronautics & Astronautics |
| 論文出版年: | 2025 |
| 畢業學年度: | 113 |
| 語文別: | 中文 |
| 論文頁數: | 148 |
| 中文關鍵詞: | 即時定位與建圖 、點到點最近鄰點搜索 、多感測器融合濾波 、因子圖優化 |
| 外文關鍵詞: | Simultaneous localization and mapping, Point-to-Point ICP, Multi-sensor Fusion Filtering, Factor Graph Optimization |
| 相關次數: | 點閱:12 下載:0 |
| 分享至: |
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1.Smith, R., M. Self, and P. Cheeseman, Estimating uncertain spatial relationships in robotics, in Autonomous robot vehicles. 1990, Springer. p. 167-193.
2.Durrant-Whyte, H., D. Rye, and E. Nebot. Localization of autonomous guided vehicles. in Robotics Research: The Seventh International Symposium. 1996. Springer.
3.Davison. Real-time simultaneous localisation and mapping with a single camera. in Proceedings Ninth IEEE International Conference on Computer Vision. 2003. IEEE.
4.Zhang, J. and S. Singh. LOAM: Lidar odometry and mapping in real-time. in Robotics: Science and systems. 2014. Berkeley, CA.
5.Cadena, C., et al., Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on robotics, 2016. 32(6): p. 1309-1332.
6.Huang, S. and G. Dissanayake, Convergence and consistency analysis for extended Kalman filter based SLAM. IEEE Transactions on robotics, 2007. 23(5): p. 1036-1049.
7.Pomerleau, F., et al., Comparing ICP variants on real-world data sets: Open-source library and experimental protocol. Autonomous robots, 2013. 34: p. 133-148.
8.Thrun, S., Probabilistic robotics. Communications of the ACM, 2002. 45(3): p. 52-57.
9.Bresson, G., et al., Simultaneous localization and mapping: A survey of current trends in autonomous driving. IEEE Transactions on Intelligent Vehicles, 2017. 2(3): p. 194-220.
10.Censi, A. An ICP variant using a point-to-line metric. in 2008 IEEE international conference on robotics and automation. 2008. Ieee.
11.Serafin, J. and G. Grisetti. NICP: Dense normal based point cloud registration. in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2015. IEEE.
12.Deschaud, J.-E. IMLS-SLAM: Scan-to-model matching based on 3D data. in 2018 IEEE International Conference on Robotics and Automation (ICRA). 2018. IEEE.
13.Peterson, L.E., K-nearest neighbor. Scholarpedia, 2009. 4(2): p. 1883.
14.Chen, Y. and G. Medioni, Object modelling by registration of multiple range images. Image and vision computing, 1992. 10(3): p. 145-155.
15.Chuang, T., Feature-based registration of LiDAR point clouds. Ph. D. Thesis, 2012.
16.Biber, P. and W. Straßer. The normal distributions transform: A new approach to laser scan matching. in Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)(Cat. No. 03CH37453). 2003. IEEE.
17.Ulaş, C. and H. Temeltaş, 3D multi-layered normal distribution transform for fast and long range scan matching. Journal of Intelligent & Robotic Systems, 2013. 71: p. 85-108.
18.Shan, T. and B. Englot. Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain. in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2018. IEEE.
19.Li, L., et al. SA-LOAM: Semantic-aided LiDAR SLAM with loop closure. in 2021 IEEE International Conference on Robotics and Automation (ICRA). 2021. IEEE.
20.Guo, H., J. Zhu, and Y. Chen, E-LOAM: LiDAR odometry and mapping with expanded local structural information. IEEE transactions on intelligent vehicles, 2022. 8(2): p. 1911-1921.
21.Wang, H., et al. F-loam: Fast lidar odometry and mapping. in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2021. IEEE.
22.Oelsch, M., M. Karimi, and E. Steinbach, R-LOAM: Improving LiDAR odometry and mapping with point-to-mesh features of a known 3D reference object. IEEE Robotics and Automation Letters, 2021. 6(2): p. 2068-2075.
23.Chang, L., X. Niu, and T. Liu, GNSS/IMU/ODO/LiDAR-SLAM integrated navigation system using IMU/ODO pre-integration. Sensors, 2020. 20(17): p. 4702.
24.Rubanov, V., et al., Development a low-cost navigation technology based on metal line sensors and passive RFID tags for industrial automated guided vehicle. Journal of Engineering and Applied Sciences, 2020. 15(20): p. 2291-2297.
25.Grisetti, G., C. Stachniss, and W. Burgard, Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE transactions on Robotics, 2007. 23(1): p. 34-46.
26.Tuna, G., et al. Evaluations of different simultaneous localization and mapping (SLAM) algorithms. in IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society. 2012. IEEE.
27.Schütz, A., D.E. Sánchez-Morales, and T. Pany. Precise positioning through a loosely-coupled sensor fusion of GNSS-RTK, INS and LiDAR for autonomous driving. in 2020 IEEE/ION position, location and navigation symposium (PLANS). 2020. IEEE.
28.Song, Y., S. Nuske, and S. Scherer, A multi-sensor fusion MAV state estimation from long-range stereo, IMU, GPS and barometric sensors. Sensors, 2016. 17(1): p. 11.
29.Júnior, G.P.C., et al., EKF-LOAM: An adaptive fusion of LiDAR SLAM with wheel odometry and inertial data for confined spaces with few geometric features. IEEE Transactions on Automation Science and Engineering, 2022. 19(3): p. 1458-1471.
30.Wen, W. and L.-T. Hsu, AGPC-SLAM: Absolute ground plane constrained 3D LiDAR SLAM. NAVIGATION: Journal of the Institute of Navigation, 2022. 69(3).
31.Kschischang, F.R., B.J. Frey, and H.-A. Loeliger, Factor graphs and the sum-product algorithm. IEEE Transactions on information theory, 2002. 47(2): p. 498-519.
32.Yang, S., et al. Equality constrained linear optimal control with factor graphs. in 2021 IEEE International Conference on Robotics and Automation (ICRA). 2021. IEEE.
33.Hao, Y., et al., Fglqr: Factor graph accelerator of lqr control for autonomous machines. arXiv preprint arXiv:2308.02768, 2023.
34.Yi, B., et al. Differentiable factor graph optimization for learning smoothers. in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2021. IEEE.
35.Vizzo, I., et al., Kiss-icp: In defense of point-to-point icp–simple, accurate, and robust registration if done the right way. IEEE Robotics and Automation Letters, 2023. 8(2): p. 1029-1036.
36.Kalman, R.E., A new approach to linear filtering and prediction problems. 1960.
37.Gelb, A., Applied optimal estimation. 1974: MIT press.
38.Crassidis, J.L. and J.L. Junkins, Optimal estimation of dynamic systems. 2004: Chapman and Hall/CRC.
39.Cho, S.Y., et al. Observability analysis of the INS/GPS navigation system on the measurements in land vehicle applications. in 2007 International Conference on Control, Automation and Systems. 2007. IEEE.
40.Gutiérrez, R., et al., A waypoint tracking controller for autonomous road vehicles using ros framework. Sensors, 2020. 20(14): p. 4062.
41.Dellaert, F. and M. Kaess, Factor graphs for robot perception. Foundations and Trends® in Robotics, 2017. 6(1-2): p. 1-139.
校內:2030-08-19公開