{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T16:40:54Z","timestamp":1769186454033,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819555666","type":"print"},{"value":"9789819555673","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-5567-3_35","type":"book-chapter","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T21:13:35Z","timestamp":1769116415000},"page":"508-522","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diffusion-Based Cross-Modal Denoising and\u00a0Reliability-Aware Deep Matching for\u00a0Robust Radar Odometry"],"prefix":"10.1007","author":[{"given":"Haoliang","family":"Feng","sequence":"first","affiliation":[]},{"given":"Haoran","family":"Li","sequence":"additional","affiliation":[]},{"given":"Lan","family":"Tang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,23]]},"reference":[{"key":"35_CR1","doi-asserted-by":"crossref","unstructured":"Adolfsson, D., Magnusson, M., Alhashimi, A., Lilienthal, A.J., Andreasson, H.: CFEAR Radarodometry \u2013 conservative filtering for efficient and accurate radar odometry. In: 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5462\u20135469. IEEE (2021)","DOI":"10.1109\/IROS51168.2021.9636253"},{"issue":"2","key":"35_CR2","doi-asserted-by":"publisher","first-page":"1476","DOI":"10.1109\/TRO.2022.3221302","volume":"39","author":"D Adolfsson","year":"2023","unstructured":"Adolfsson, D., Magnusson, M., Alhashimi, A., Lilienthal, A.J., Andreasson, H.: Lidar-level localization with radar? The CFEAR approach to accurate, fast, and robust large-scale radar odometry in diverse environments. IEEE Trans. Rob. 39(2), 1476\u20131495 (2023)","journal-title":"IEEE Trans. Rob."},{"issue":"3","key":"35_CR3","doi-asserted-by":"publisher","first-page":"7865","DOI":"10.1109\/LRA.2022.3186757","volume":"7","author":"R Aldera","year":"2022","unstructured":"Aldera, R., Gadd, M., De Martini, D., Newman, P.: What goes around: leveraging a constant-curvature motion constraint in radar odometry. IEEE Robot. Autom. Lett. 7(3), 7865\u20137872 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"Barnes, D., Gadd, M., Murcutt, P., Newman, P., Posner, I.: The Oxford Radar RobotCar Dataset: a radar extension to the Oxford Radar RobotCar Dataset. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 6433\u20136438. IEEE (2020)","DOI":"10.1109\/ICRA40945.2020.9196884"},{"key":"35_CR5","unstructured":"Barnes, D., Weston, R., Posner, I.: Masking by moving: Learning distraction-free radar odometry from pose information. arXiv preprint arXiv:1909.03752 (2019)"},{"issue":"2","key":"35_CR6","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1109\/LRA.2021.3052439","volume":"6","author":"K Burnett","year":"2021","unstructured":"Burnett, K., Schoellig, A.P., Barfoot, T.D.: Do we need to compensate for motion distortion and doppler effects in spinning radar navigation? IEEE Robot. Autom. Lett. 6(2), 771\u2013778 (2021)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"35_CR7","doi-asserted-by":"publisher","unstructured":"Burnett, K., Yoon, D.J., Schoellig, A.P., Barfoot, T.: Radar odometry combining probabilistic estimation and unsupervised feature learning. In: Proceedings of Robotics: Science and Systems. Virtual, July 2021 (2021). https:\/\/doi.org\/10.15607\/RSS.2021.XVII.029","DOI":"10.15607\/RSS.2021.XVII.029"},{"key":"35_CR8","doi-asserted-by":"publisher","unstructured":"Checchin, P., G\u00e9rossier, F., Blanc, C., Chapuis, R., Trassoudaine, L.: Radar scan matching slam using the Fourier-Mellin transform. In: Howard, A., Iagnemma, K., Kelly, A. (eds.) Field and Service Robotics: Results of the 7th International Conference. Springer Tracts in Advanced Robotics, vol. 62, pp. 151\u2013161. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-13408-1_14","DOI":"10.1007\/978-3-642-13408-1_14"},{"key":"35_CR9","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780\u20138794 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"3","key":"35_CR10","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1016\/0047-259X(82)90077-X","volume":"12","author":"D Dowson","year":"1982","unstructured":"Dowson, D., Landau, B.: The Fr\u00e9chet distance between multivariate normal distributions. J. Multivar. Anal. 12(3), 450\u2013455 (1982)","journal-title":"J. Multivar. Anal."},{"key":"35_CR11","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354\u20133361. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"issue":"3","key":"35_CR12","doi-asserted-by":"publisher","first-page":"6020","DOI":"10.1109\/LRA.2022.3162644","volume":"7","author":"K Haggag","year":"2022","unstructured":"Haggag, K., Lange, S., Pfeifer, T., Protzel, P.: A credible and robust approach to ego-motion estimation using an automotive radar. IEEE Robot. Autom. Lett. 7(3), 6020\u20136027 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"35_CR13","doi-asserted-by":"crossref","unstructured":"Hilger, M., Mandischer, N., Corves, B.: RaNDT SLAM: Radar SLAM based on intensity-augmented normal distributions transform. In: 2024 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7831\u20137838. IEEE (2024)","DOI":"10.1109\/IROS58592.2024.10802458"},{"issue":"5","key":"35_CR14","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1177\/02783649221080483","volume":"41","author":"Z Hong","year":"2022","unstructured":"Hong, Z., Petillot, Y., Wallace, A., Wang, S.: RadarSLAM: a robust simultaneous localization and mapping system for all weather conditions. Int. J. Robot. Res. 41(5), 519\u2013542 (2022)","journal-title":"Int. J. Robot. Res."},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"Hong, Z., Petillot, Y., Wang, S.: RadarSLAM: radar based large-scale slam in all weathers. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5164\u20135170. IEEE (2020)","DOI":"10.1109\/IROS45743.2020.9341287"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Kung, P.C., Wang, C.C., Lin, W.C.: A normal distribution transform-based radar odometry designed for scanning and automotive radars. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 14417\u201314423. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9561413"},{"issue":"2","key":"35_CR17","doi-asserted-by":"publisher","first-page":"2637","DOI":"10.1109\/LRA.2022.3144528","volume":"7","author":"PC Kung","year":"2022","unstructured":"Kung, P.C., Wang, C.C., Lin, W.C.: Radar occupancy prediction with lidar supervision while preserving long-range sensing and penetrating capabilities. IEEE Robot. Autom. Lett. 7(2), 2637\u20132643 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Lim, H., Han, K., Shin, G., Kim, G., Hong, S., Myung, H.: ORORA: outlier-robust radar odometry. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 2046\u20132053. IEEE (2023)","DOI":"10.1109\/ICRA48891.2023.10160997"},{"key":"35_CR19","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"35_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A ConvNet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"35_CR21","doi-asserted-by":"crossref","unstructured":"Lubanco, D.L.S., Hashem, A., Pichler-Scheder, M., Stelzer, A., Feger, R., Schlechter, T.: R 3 O: robust radon radar odometry. IEEE Trans. Intell. Veh. (2023)","DOI":"10.1109\/TIV.2023.3324941"},{"key":"35_CR22","doi-asserted-by":"crossref","unstructured":"Park, Y.S., Shin, Y.S., Kim, A.: PhaRaO: direct radar odometry using phase correlation. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 2617\u20132623. IEEE (2020)","DOI":"10.1109\/ICRA40945.2020.9197231"},{"issue":"04","key":"35_CR23","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1109\/34.88573","volume":"13","author":"S Umeyama","year":"1991","unstructured":"Umeyama, S.: Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. Mach. Intell. 13(04), 376\u2013380 (1991)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"35_CR24","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"35_CR25","doi-asserted-by":"crossref","unstructured":"Weston, R., Gadd, M., De\u00a0Martini, D., Newman, P., Posner, I.: Fast-MByM: leveraging translational invariance of the Fourier transform for efficient and accurate radar odometry. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 2186\u20132192. IEEE (2022)","DOI":"10.1109\/ICRA46639.2022.9812063"},{"key":"35_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"issue":"3","key":"35_CR27","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1109\/LRA.2023.3236570","volume":"8","author":"R Zhang","year":"2023","unstructured":"Zhang, R., Zhang, Y., Fu, D., Liu, K.: Scan denoising and normal distribution transform for accurate radar odometry and positioning. IEEE Robot. Autom. Lett. 8(3), 1199\u20131206 (2023)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"35_CR28","unstructured":"Zhao, W., Bai, L., Rao, Y., Zhou, J., Lu, J.: UniC: a unified predictor-corrector framework for fast sampling of diffusion models. Adv. Neural Inf. Process. Syst. 36 (2024)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5567-3_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T21:13:39Z","timestamp":1769116419000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5567-3_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819555666","9789819555673"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5567-3_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"23 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}