GNSS-denied Navigation for UAVs: A Systematic Literature Review of Inertial, Visual, and Hybrid Solutions

Saravanan Murugesan, Jueying Li

Abstract


The reliance on Global Navigation Satellite Systems (GNSS) poses significant limitations for UAV operations in GPS-denied or degraded environments such as indoors, urban canyons, and dense forests. This systematic literature review (SLR) examines over 150 studies addressing GNSS-denied navigation solutions for UAVs, focusing on inertial navigation systems (INS), visual odometry (VO), LiDAR-based localization, and hybrid sensor fusion approaches. The review categorizes methods based on sensor configurations, algorithmic frameworks, and application scenarios. INS-based methods provide high-frequency state updates but suffer from drift accumulation, while VO and LiDAR offer drift correction through environmental feature tracking. Hybrid approaches leverage complementary strengths, employing Extended Kalman Filters, factor graphs, or optimization-based fusion to improve robustness and accuracy. The review highlights challenges such as scale ambiguity, feature sparsity, and computational constraints on embedded platforms. Validation methodologies range from controlled indoor experiments to complex outdoor scenarios, with increasing adoption of public datasets and benchmarks. The paper identifies emerging trends in deep learning for feature extraction and loop closure detection, as well as the integration of semantic information for improved localization. By synthesizing current knowledge and identifying open challenges, this SLR provides a roadmap for advancing GNSS-denied navigation capabilities critical for expanding UAV operational domains.

References


Cadena, Cesar, Luca Carlone, Henry Carrillo, Yasir Latif, Davide Scaramuzza, José Neira, Ian Reid, and John J. Leonard. “Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age”. IEEE Transactions on Robotics 32, no. 6 (2016): 1309–32. https://doi.org/10.1109/TRO.2016.2624754.

Qin, Tong, Peiliang Li, and Shaojie Shen. “VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator”. IEEE Transactions on Robotics 34, no. 4 (2018): 1004–20. https://doi.org/10.1109/TRO.2018.2853729.

Mur-Artal, Raúl, and Juan D. Tardós. “ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras”. IEEE Transactions on Robotics 33, no. 5 (2017): 1255–62. https://doi.org/10.1109/TRO.2017.2705103.

Campos, Carlos, Richard Elvira, Juan J. Gómez Rodríguez, José M. M. Montiel, and Juan D. Tardós. “ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM”. IEEE Transactions on Robotics 37, no. 6 (2021): 1874–90. https://doi.org/10.1109/TRO.2021.3075644.

Forster, Christian, Luca Carlone, Frank Dellaert, and Davide Scaramuzza. “On-Manifold Preintegration for Real-Time Visual-Inertial Odometry”. IEEE Transactions on Robotics 33, no. 1 (2017): 1–21. https://doi.org/10.1109/TRO.2016.2597321.

Shan, Tixiao, Brendan Englot, Drew Meyers, Wei Wang, Carlo Ratti, and Daniela Rus. “LIO-SAM: Tightly-Coupled Lidar Inertial Odometry via Smoothing and Mapping”. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5135–42, 2020. https://doi.org/10.1109/IROS45743.2020.9341176.

Zhang, Ji, Sanjiv Singh, and Others. “LOAM: Lidar Odometry and Mapping in Real-Time”. In Robotics: Science and Systems, 2:1–9. https://doi.org/10.15607/RSS.2014.X.007.

Burri, Michael, Janosch Nikolic, Pascal Gohl, Thomas Schneider, Joern Rehder, Sammy Omari, Markus W. Achtelik, and Roland Siegwart. “The EuRoC Micro Aerial Vehicle Datasets”. The International Journal of Robotics Research 35, no. 10 (2016): 1157–63. https://doi.org/10.1177/0278364915620033.

Arshad, Saba, and Gon-Woo Kim. “Role of Deep Learning in Loop Closure Detection for Visual and Lidar SLAM: A Survey”. Sensors 21, no. 4 (2021). https://doi.org/10.3390/s21041243.

Mur-Artal, Raúl, J. M. M. Montiel, and Juan D. Tardós. “ORB-SLAM: A Versatile and Accurate Monocular SLAM System”. IEEE Transactions on Robotics 31, no. 5 (2015): 1147–63. https://doi.org/10.1109/TRO.2015.2463671.

Delmerico, Jeffrey, and Davide Scaramuzza. “A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots”. In 2018 IEEE International Conference on Robotics and Automation (ICRA), 2502–9, 2018. https://doi.org/10.1109/ICRA.2018.8460664.

Couturier, Andy, and Moulay A. Akhloufi. “A Review on Absolute Visual Localization for UAV”. Robotics and Autonomous Systems 135 (2021): 103666. https://doi.org/10.1016/j.robot.2020.103666.

Shan, Tixiao, and Brendan 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), 4758–65, 2018. https://doi.org/10.1109/IROS.2018.8594299.


Refbacks

  • There are currently no refbacks.


Bookmark and Share


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.