{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:56:30Z","timestamp":1784998590740,"version":"3.55.0"},"reference-count":84,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"109","funder":[{"DOI":"10.13039\/100006754","name":"U.S. Army Research Laboratory","doi-asserted-by":"publisher","award":["W911NF-23-S-0001"],"award-info":[{"award-number":["W911NF-23-S-0001"]}],"id":[{"id":"10.13039\/100006754","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006754","name":"U.S. Army Research Laboratory","doi-asserted-by":"publisher","award":["W911NF2120152"],"award-info":[{"award-number":["W911NF2120152"]}],"id":[{"id":"10.13039\/100006754","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006754","name":"U.S. Army Research Laboratory","doi-asserted-by":"publisher","award":["W911NF2420125"],"award-info":[{"award-number":["W911NF2420125"]}],"id":[{"id":"10.13039\/100006754","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006754","name":"U.S. Army Research Lab","doi-asserted-by":"crossref","award":["W911NF-17-S-0003"],"award-info":[{"award-number":["W911NF-17-S-0003"]}],"id":[{"id":"10.13039\/100006754","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.science.org"],"crossmark-restriction":true},"short-container-title":["Sci. Robot."],"published-print":{"date-parts":[[2025,12,10]]},"abstract":"<jats:p>Resilient and robust odometry is crucial for autonomous systems operating in complex and dynamic environments. Existing odometry systems often struggle with severe sensory degradations and extreme conditions such as smoke, sandstorms, snow, or low-light conditions, threatening both the safety and functionality of robots. To address these challenges, we present Super Odometry, a sensor fusion framework that dynamically adapts to varying levels of environmental degradation. Super Odometry uses a hierarchical structure to integrate four core modules from lower-level to higher-level adaptability, including adaptive feature selection, adaptive state direction selection, adaptive engine selection, and a learning-based inertial odometry. The inertial odometry, trained on more than 100 hours of heterogeneous robotic platforms, captures comprehensive motion dynamics. Super Odometry elevates the inertial measurement unit to equal importance with camera and light detection and ranging (LiDAR) systems in the sensor fusion framework, providing a reliable fallback when exteroceptive sensors fail. Super Odometry has been validated across 200 kilometers and 800 operational hours on a fleet of aerial, wheeled, and legged robots and under diverse sensor configurations, environmental degradation, and aggressive motion profiles. It marks an important step toward safe and long-term robotic autonomy in all-degraded environments.<\/jats:p>","DOI":"10.1126\/scirobotics.adv1818","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T19:01:34Z","timestamp":1765393294000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":3,"title":["Resilient odometry via hierarchical adaptation"],"prefix":"10.1126","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1801-8156","authenticated-orcid":true,"given":"Shibo","family":"Zhao","sequence":"first","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3602-7566","authenticated-orcid":true,"given":"Sifan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6250-9141","authenticated-orcid":true,"given":"Yuchen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4692-5645","authenticated-orcid":true,"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4630-0805","authenticated-orcid":true,"given":"Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"University at Buffalo, Buffalo, NY, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4488-5619","authenticated-orcid":true,"given":"Wenshan","family":"Wang","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8373-4688","authenticated-orcid":true,"given":"Sebastian","family":"Scherer","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/504729.504754"},{"key":"e_1_3_3_3_2","doi-asserted-by":"crossref","unstructured":"J. Levinson J. Askeland J. Becker J. Dolson D. Held S. Kammel J. Zico Kolter D. Langer O. Pink V. Pratt M. Sokolsky G. Stanek D. Stavens A. Teichman M. Werling S. Thrun \u201cTowards fully autonomous driving: Systems and algorithms\u201d in IEEE Intelligent Vehicles Symposium (IV) (IEEE 2011) pp. 163\u2013168.","DOI":"10.1109\/IVS.2011.5940562"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.55417\/fr.2022023"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3187826"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2016.2624754"},{"key":"e_1_3_3_7_2","doi-asserted-by":"crossref","unstructured":"S. Shen Y. Mulgaonkar N. Michael V. Kumar \u201cMulti-sensor fusion for robust autonomous flight in indoor and outdoor environments with a rotorcraft MAV\u201d in 2014 IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2014) pp. 4974\u20134981.","DOI":"10.1109\/ICRA.2014.6907588"},{"key":"e_1_3_3_8_2","doi-asserted-by":"crossref","unstructured":"E. A. Wan R. Van Der Merwe S. Haykin \u201cThe unscented Kalman filter\u201d in Kalman Filtering and Neural Networks (Wiley 2001) pp. 221\u2013280.","DOI":"10.1002\/0471221546.ch7"},{"key":"e_1_3_3_9_2","doi-asserted-by":"crossref","unstructured":"J. Zhang S. Singh \u201cLOAM: LiDAR odometry and mapping in real-time\u201d in Robotics: Science and Systems (RSS 2014) vol. 2 p. 07.","DOI":"10.15607\/RSS.2014.X.007"},{"key":"e_1_3_3_10_2","doi-asserted-by":"crossref","unstructured":"J. Zhang S. Singh \u201cVisual-lidar odometry and mapping: Low-drift robust and fast\u201d in 2015 IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2015) pp. 2174\u20132181.","DOI":"10.1109\/ICRA.2015.7139486"},{"key":"e_1_3_3_11_2","doi-asserted-by":"crossref","unstructured":"S. Khattak H. Nguyen F. Mascarich T. Dang K. Alexis \u201cComplementary multi\u2013modal sensor fusion for resilient robot pose estimation in subterranean environments\u201d in 2020 International Conference on Unmanned Aircraft Systems (ICUAS) (IEEE 2020) pp. 1024\u20131029.","DOI":"10.1109\/ICUAS48674.2020.9213865"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2020.00068"},{"key":"e_1_3_3_13_2","doi-asserted-by":"crossref","unstructured":"J. Graeter A. Wilczynski M. Lauer \u201cLimo: Lidar-monocular visual odometry\u201d in 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2018) pp. 7872\u20137879.","DOI":"10.1109\/IROS.2018.8594394"},{"key":"e_1_3_3_14_2","doi-asserted-by":"crossref","unstructured":"X. Zuo P. Geneva W. Lee Y. Liu G. Huang \u201cLic-fusion: Lidar-inertial-camera odometry\u201d in 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2019) pp. 5848\u20135854.","DOI":"10.1109\/IROS40897.2019.8967746"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3056380"},{"key":"e_1_3_3_16_2","doi-asserted-by":"crossref","unstructured":"J. Lin F. Zhang \u201cR3LIVE: A robust real-time RGB-colored LiDAR-inertial-visual tightly-coupled state estimation and mapping package\u201d in 2022 International Conference on Robotics and Automation (ICRA) (IEEE 2022) pp. 10672\u201310678.","DOI":"10.1109\/ICRA46639.2022.9811935"},{"key":"e_1_3_3_17_2","doi-asserted-by":"crossref","unstructured":"S. Zhao Y. Gao T. Wu D. Singh R. Jiang H. Sun M. Sarawata Y. Qiu W. Whittaker I. Higgins Y. Du S. Su C. Xu J. Keller J. Karhade L. Nogueira S. Saha J. Zhang W. Wang C. Wang S. Scherer \u201cSubT-MRS dataset: Pushing SLAM towards all-weather environments\u201d in IEEE\/CVF Conference on Computer Vision and Pattern Recognition (IEEE 2024) pp. 22647\u201322657.","DOI":"10.1109\/CVPR52733.2024.02137"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2023.3323938"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2020.571394"},{"key":"e_1_3_3_20_2","article-title":"Imperative learning: A self-supervised neural-symbolic learning framework for robot autonomy","volume":"2025","author":"Wang C.","year":"2025","unstructured":"C. Wang, K. Ji, J. Geng, Z. Ren, T. Fu, F. Yang, Y. Guo, H. He, X. Chen, Z. Zhan, Q. du, S. Su, B. Li, Y. Qiu, Y. du, Q. Li, Y. Yang, X. Lin, Z. Zhao, Imperative learning: A self-supervised neural-symbolic learning framework for robot autonomy. Int. J. Robot. Res. 2025, 10.1177\/02783649251353181 (2025).","journal-title":"Int. J. Robot. Res."},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1561\/2300000043"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2017.2777002"},{"key":"e_1_3_3_23_2","doi-asserted-by":"crossref","unstructured":"S. Zhao P. Wang H. Zhang Z. Fang S. Scherer \u201cTp-tio: A robust thermal-inertial odometry with deep thermalpoint\u201d in 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2020) pp. 4505\u20134512.","DOI":"10.1109\/IROS45743.2020.9341716"},{"key":"e_1_3_3_24_2","doi-asserted-by":"crossref","unstructured":"P. Geneva K. Eckenhoff W. Lee Y. Yang G. Huang \u201cOpenVINS: A research platform for visual-inertial estimation\u201d in 2020 IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2020) pp. 4666\u20134672.","DOI":"10.1109\/ICRA40945.2020.9196524"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2018.2853729"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3064227"},{"key":"e_1_3_3_27_2","doi-asserted-by":"crossref","unstructured":"A. Segal D. Haehnel S. Thrun \u201cGeneralized-ICP\u201d in Robotics: Science and Systems (RSS 2009) vol. 2 p. 435.","DOI":"10.15607\/RSS.2009.V.021"},{"key":"e_1_3_3_28_2","doi-asserted-by":"crossref","unstructured":"C. Zheng Q. Zhu W. Xu X. Liu Q. Guo F. Zhang \u201cFAST-LIVO: Fast and tightly-coupled sparse-direct LiDAR-inertial-visual odometry\u201d in 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2022) pp. 4003\u20134009.","DOI":"10.1109\/IROS47612.2022.9981107"},{"key":"e_1_3_3_29_2","doi-asserted-by":"crossref","unstructured":"T. Shan B. Englot D. Meyers W. Wang C. Ratti D. Rus \u201cLIO-SAM: Tightly-coupled Lidar inertial odometry via smoothing and mapping\u201d in 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2020) pp. 5135\u20135142.","DOI":"10.1109\/IROS45743.2020.9341176"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3252342"},{"key":"e_1_3_3_31_2","doi-asserted-by":"crossref","unstructured":"M. Sivaprakasam P. Maheshwari M. G. Castro S. Triest M. Nye S. Willits A. Saba W. Wang S. Scherer TartanDrive 2.0: More modalities and better infrastructure to further self-supervised learning research in off-road driving tasks. arXiv:2402.01913 [cs.RO] (2024); https:\/\/arxiv.org\/abs\/2402.01913.","DOI":"10.1109\/ICRA57147.2024.10611265"},{"key":"e_1_3_3_32_2","doi-asserted-by":"crossref","unstructured":"C. Chen X. Lu A. Markham N. Trigoni \u201cIonet: Learning to cure the curse of drift in inertial odometry\u201d in AAAI Conference on Artificial Intelligence (PKP Publishing Services 2018) vol. 32; https:\/\/doi.org\/10.1609\/aaai.v32i1.12102.","DOI":"10.1609\/aaai.v32i1.12102"},{"key":"e_1_3_3_33_2","doi-asserted-by":"crossref","unstructured":"X. Liu Z. Gao H. Cheng P. Wang B. M. Chen \u201cLearning-based low light image enhancement for visual odometry\u201d in 2020 IEEE 16th International Conference on Control & Automation (ICCA) (IEEE 2020) pp. 1143\u20131148.","DOI":"10.1109\/ICCA51439.2020.9264401"},{"key":"e_1_3_3_34_2","doi-asserted-by":"crossref","unstructured":"D. Chen N. Wang R. Xu W. Xie H. Bao G. Zhang \u201cRNIN-VIO: Robust neural inertial navigation aided visual-inertial odometry in challenging scenes\u201d in 2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (IEEE 2021) pp. 275\u2013283.","DOI":"10.1109\/ISMAR52148.2021.00043"},{"key":"e_1_3_3_35_2","doi-asserted-by":"crossref","unstructured":"H. Yan Q. Shan Y. Furukawa \u201cRIDI: Robust IMU double integration\u201d in European Conference on Computer Vision (ECCV) (ECVA 2018) pp. 621\u2013636.","DOI":"10.1007\/978-3-030-01261-8_38"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2020.2980758"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3007421"},{"key":"e_1_3_3_38_2","doi-asserted-by":"crossref","unstructured":"J. Sturm N. Engelhard F. Endres W. Burgard D. Cremers \u201cA benchmark for the evaluation of RGB-D SLAM systems\u201d in 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2012) pp. 573\u2013580.","DOI":"10.1109\/IROS.2012.6385773"},{"key":"e_1_3_3_39_2","doi-asserted-by":"crossref","unstructured":"S. Zhao H. Zhang P. Wang L. Nogueira S. Scherer \u201cSuper odometry: IMU-centric LiDAR-visual-inertial estimator for challenging environments\u201d in 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2021) pp. 8729\u20138736.","DOI":"10.1109\/IROS51168.2021.9635862"},{"key":"e_1_3_3_40_2","unstructured":"A. Prorok M. Malencia L. Carlone G. S. Sukhatme B. M. Sadler V. Kumar Beyond robustness: A taxonomy of approaches towards resilient multi-robot systems. arXiv:2109.12343 [cs.RO] (2021); https:\/\/arxiv.org\/abs\/2109.12343."},{"key":"e_1_3_3_41_2","doi-asserted-by":"crossref","unstructured":"C. Forster L. Carlone F. Dellaert D. Scaramuzza \u201cIMU preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation\u201d in Robotics: Science and Systems XI (RSS 2015) p. 06.","DOI":"10.15607\/RSS.2015.XI.006"},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2022.3174476"},{"key":"e_1_3_3_43_2","doi-asserted-by":"crossref","unstructured":"P. Furgale T. D. Barfoot G. Sibley \u201cContinuous-time batch estimation using temporal basis functions\u201d in 2012 IEEE International Conference on Robotics and Automation (IEEE 2012) pp. 2088\u20132095.","DOI":"10.1109\/ICRA.2012.6225005"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364915620033"},{"key":"e_1_3_3_45_2","doi-asserted-by":"crossref","unstructured":"M. Hwangbo J.-S. Kim T. Kanade \u201cInertial-aided KLT feature tracking for a moving camera\u201d in 2009 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IEEE 2009) pp. 1909\u20131916.","DOI":"10.1109\/IROS.2009.5354093"},{"key":"e_1_3_3_46_2","doi-asserted-by":"crossref","unstructured":"D. Muhle L. Koestler N. Demmel F. Bernard D. Cremers \u201cThe probabilistic normal epipolar constraint for frame-to-frame rotation optimization under uncertain feature positions\u201d in IEEE\/CVF Conference on Computer Vision and Pattern Recognition (IEEE 2022) pp. 1819\u20131828.","DOI":"10.1109\/CVPR52688.2022.00186"},{"key":"e_1_3_3_47_2","doi-asserted-by":"crossref","unstructured":"J. Demantk\u00e9 C. Mallet N. David B. Vallet Dimensionality based scale selection in 3D lidar point clouds in Laserscanning (University of Calgary 2011) vol. XXXVIII-5\/W12 pp. 97\u2013102.","DOI":"10.5194\/isprsarchives-XXXVIII-5-W12-97-2011"},{"key":"e_1_3_3_48_2","doi-asserted-by":"crossref","unstructured":"T. Shan B. Englot C. Ratti D. Rus \u201cLVI-SAM: Tightly-coupled LiDAR-visual-inertial odometry via smoothing and mapping\u201d in 2021 IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2021) pp. 5692\u20135698.","DOI":"10.1109\/ICRA48506.2021.9561996"},{"key":"e_1_3_3_49_2","doi-asserted-by":"crossref","unstructured":"J. Zhang M. Kaess S. Singh \u201cOn degeneracy of optimization-based state estimation problems\u201d in 2016 IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2016) pp. 809\u2013816.","DOI":"10.1109\/ICRA.2016.7487211"},{"key":"e_1_3_3_50_2","doi-asserted-by":"crossref","unstructured":"W. Zhen S. Scherer \u201cEstimating the localizability in tunnel-like environments using LiDAR and UWB\u201d in 2019 International Conference on Robotics and Automation (ICRA) (IEEE 2019) pp. 4903\u20134908.","DOI":"10.1109\/ICRA.2019.8794167"},{"key":"e_1_3_3_51_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21124042"},{"key":"e_1_3_3_52_2","doi-asserted-by":"crossref","unstructured":"A. Hinduja B.-J. Ho M. Kaess \u201cDegeneracy-aware factors with applications to underwater SLAM\u201d in 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2019) pp. 1293\u20131299.","DOI":"10.1109\/IROS40897.2019.8968577"},{"key":"e_1_3_3_53_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-021-01362-w"},{"key":"e_1_3_3_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3061331"},{"key":"e_1_3_3_55_2","unstructured":"M. Ramezani K. Khosoussi G. Catt P. Moghadam J. Williams P. Borges F. Pauling N. Kottege Wildcat: Online continuous-time 3D LiDAR-inertial slam. arXiv:2205.12595 [cs.RO] (2022); https:\/\/arxiv.org\/abs\/2205.12595."},{"key":"#cr-split#-e_1_3_3_56_2.1","doi-asserted-by":"crossref","unstructured":"H. Lim D. Kim B. Kim H. Myung \"AdaLIO: Robust adaptive LiDAR-inertial odometry in degenerate indoor environments\" in 2023 International Conference on Ubiquitous Robots","DOI":"10.1109\/UR57808.2023.10202252"},{"key":"#cr-split#-e_1_3_3_56_2.2","unstructured":"(UR) (Korea Robotics Society 2023) pp. 48-53."},{"key":"e_1_3_3_57_2","doi-asserted-by":"crossref","unstructured":"G. Best R. Garg J. Keller G. A. Hollinger S. Scherer \u201cResilient multi-sensor exploration of multifarious environments with a team of aerial robots\u201d in Robotics: Science and Systems (RSS) (RSS 2022) p. 004.","DOI":"10.15607\/RSS.2022.XVIII.004"},{"key":"e_1_3_3_58_2","doi-asserted-by":"crossref","unstructured":"R. Nemiroff K. Chen B. T. Lopez \u201cJoint on-manifold gravity and accelerometer intrinsics estimation for inertially aligned mapping\u201d in 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2023) pp. 1388\u20131394.","DOI":"10.1109\/IROS55552.2023.10342424"},{"key":"e_1_3_3_59_2","doi-asserted-by":"crossref","unstructured":"S. Sun D. Melamed K. Kitani \u201cIDOL: Inertial deep orientation-estimation and localization\u201d in AAAI Conference on Artificial Intelligence (PKP Publishing Services 2021) vol. 35 pp. 6128\u20136137.","DOI":"10.1609\/aaai.v35i7.16763"},{"key":"e_1_3_3_60_2","doi-asserted-by":"crossref","unstructured":"A. Antonini W. Guerra V. Murali T. Sayre-McCord S. Karaman \u201cThe Blackbird dataset: A large-scale dataset for UAV perception in aggressive flight\u201d in International Symposium on Experimental Robotics (Springer 2020) pp. 130\u2013139.","DOI":"10.1007\/978-3-030-33950-0_12"},{"key":"e_1_3_3_61_2","doi-asserted-by":"crossref","unstructured":"J. Delmerico T. Cieslewski H. Rebecq M. Faessler D. Scaramuzza \u201cAre we ready for autonomous drone racing? The UZH-FPV drone racing dataset\u201d in 2019 IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2019) pp. 6713\u20136719.","DOI":"10.1109\/ICRA.2019.8793887"},{"key":"e_1_3_3_62_2","doi-asserted-by":"crossref","unstructured":"X. Cao C. Zhou D. Zeng Y. Wang \u201cRIO: Rotation-equivariance supervised learning of robust inertial odometry\u201d in IEEE\/CVF Conference on Computer Vision and Pattern Recognition (IEEE 2022) pp. 6614\u20136623.","DOI":"10.1109\/CVPR52688.2022.00650"},{"key":"e_1_3_3_63_2","unstructured":"E. J. Hu Y. Shen P. Wallis Z. Allen-Zhu Y. Li S. Wang L. Wang W. Chen \u201cLoRA: Low-rank adaptation of large language models\u201d in International Conference on Learning Representations (ICLR 2022) pp. 1\u201313."},{"key":"e_1_3_3_64_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364920908331"},{"key":"e_1_3_3_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2022.3141876"},{"key":"e_1_3_3_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2023.3311671"},{"key":"e_1_3_3_67_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2025.01.036"},{"key":"e_1_3_3_68_2","doi-asserted-by":"crossref","unstructured":"H. Lim S. Yeon S. Ryu Y. Lee Y. KIm J. Yun E. Jung D. Lee H. Myung \u201cA single correspondence is enough: Robust global registration to avoid degeneracy in urban environments\u201d in 2022 IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2022) pp. 8010\u20138017.","DOI":"10.1109\/ICRA46639.2022.9812018"},{"key":"e_1_3_3_69_2","doi-asserted-by":"crossref","unstructured":"Y. Wu T. Guadagnino L. Wiesmann L. Klingbeil C. Stachniss H. Kuhlmann \u201cLIO-EKF: High frequency LiDAR-inertial odometry using extended Kalman filters \u201d 2024 IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2024) pp. 13741\u201313747.","DOI":"10.1109\/ICRA57147.2024.10610667"},{"key":"e_1_3_3_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3142739"},{"key":"e_1_3_3_71_2","doi-asserted-by":"crossref","unstructured":"G. Kim A. Kim \u201cScan context: Egocentric spatial descriptor for place recognition within 3D point cloud map\u201d in 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2018) pp. 4802\u20134809.","DOI":"10.1109\/IROS.2018.8593953"},{"key":"e_1_3_3_72_2","doi-asserted-by":"crossref","unstructured":"S. Zhao S. Zhou Y. Zhang J. Zhang C. Wang W. Wang S. Scherer Resilient odometry via hierarchical adaptation Zenodo (2025); https:\/\/doi.org\/10.5281\/zenodo.17569700.","DOI":"10.1126\/scirobotics.adv1818"},{"key":"e_1_3_3_73_2","doi-asserted-by":"crossref","unstructured":"A. Tagliabue J. Tordesillas X. Cai A. Santamaria-Navarro J.P. How L. Carlone A.A. Agha-mohammadi \u201cLion: LiDAR-inertial observability-aware navigator for vision-denied environments\u201d in Experimental Robotics: The 17th International Symposium (Springer 2021) pp. 380\u2013390.","DOI":"10.1007\/978-3-030-71151-1_34"},{"key":"e_1_3_3_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2023.3335691"},{"key":"e_1_3_3_75_2","doi-asserted-by":"crossref","unstructured":"W. Talbot J. Nash M. Paton E. Ambrose B. Metz R. Thakker R. Etheredge M. Ono V. Ila \u201cPrincipled ICP covariance modelling in perceptually degraded environments for the EELS mission concept\u201d in 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2023) pp. 10763\u201310770.","DOI":"10.1109\/IROS55552.2023.10341455"},{"key":"e_1_3_3_76_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2965391"},{"key":"e_1_3_3_77_2","unstructured":"J. Nubert T. Tuna J. Frey C. Cadena K. J. Kuchenbecker S. Khattak M. Hutter Holistic fusion: Task-and setup-agnostic robot localization and state estimation with factor graphs. arXiv:2504.06479 [cs.RO] (2025); https:\/\/arxiv.org\/abs\/2504.06479."},{"key":"e_1_3_3_78_2","doi-asserted-by":"crossref","unstructured":"X. Zhong Y. Li S. Zhu W. Chen X. Li J. Gu \u201cLVIO-SAM: A multi-sensor fusion odometry via smoothing and mapping\u201d in 2021 IEEE International Conference on Robotics and Biomimetics (ROBIO) (IEEE 2021) pp. 440\u2013445.","DOI":"10.1109\/ROBIO54168.2021.9739244"},{"key":"e_1_3_3_79_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3222956"},{"key":"e_1_3_3_80_2","unstructured":"N. Gelfand L. Ikemoto S. Rusinkiewicz M. Levoy \u201cGeometrically stable sampling for the ICP algorithm\u201d in International Conference on 3-D Digital Imaging and Modeling (3DIM) (IEEE 2003) pp. 260\u2013267."},{"key":"e_1_3_3_81_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3355778"},{"key":"e_1_3_3_82_2","unstructured":"X. Zheng J. Zhu Traj-LIO: A resilient multi-LiDAR multi-IMU state estimator through sparse Gaussian process. arXiv:2402.09189 [cs.RO] (2024); https:\/\/arxiv.org\/abs\/2402.09189."},{"key":"e_1_3_3_83_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2021.3075644"},{"key":"e_1_3_3_84_2","doi-asserted-by":"crossref","unstructured":"M. Dreissig D. Scheuble F. Piewak J. Boedecker Survey on LiDAR perception in adverse weather conditions. arXiv:2304.06312 [cs.RO] (2023); https:\/\/arxiv.org\/abs\/2304.06312.","DOI":"10.1109\/IV55152.2023.10186539"}],"container-title":["Science Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.science.org\/doi\/pdf\/10.1126\/scirobotics.adv1818","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T14:22:10Z","timestamp":1767968530000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.science.org\/doi\/10.1126\/scirobotics.adv1818"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,10]]},"references-count":84,"journal-issue":{"issue":"109","published-print":{"date-parts":[[2025,12,10]]}},"alternative-id":["10.1126\/scirobotics.adv1818"],"URL":"https:\/\/doi.org\/10.1126\/scirobotics.adv1818","relation":{},"ISSN":["2470-9476"],"issn-type":[{"value":"2470-9476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,10]]},"assertion":[{"value":"2025-01-04","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-13","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"eadv1818"}}