{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T01:21:06Z","timestamp":1775611266870,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T00:00:00Z","timestamp":1703635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Basic Research Program","award":["57"],"award-info":[{"award-number":["57"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>This paper addresses a systematic method for odometry calibration of a differential-drive mobile robot moving on arbitrary paths in the presence of slippage and an algorithm encoding it which is well fit for online applications. It exploits the redundancy of sensors commonly available on ground mobile robots, such as encoders, gyroscopes, and IMU, to promptly detect slippage phenomena during the calibration process and effectively address their impact on odometry. The proposed technique has been validated through exhaustive numerical simulations and compared with other available odometry calibration methods. The simulation results confirm that the proposed methodology mitigates the impact of poor calibration, conducted without considering possible slipping phenomena, on reaching a target position, reducing the error by up to a maximum of 35 times. This restores the robot\u2019s performance to a calibration condition close to that of a slip-free scenario, confirming the effectiveness of the approach and its robustness against slippage phenomena.<\/jats:p>","DOI":"10.3390\/robotics13010007","type":"journal-article","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T07:45:32Z","timestamp":1703663132000},"page":"7","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Online Odometry Calibration for Differential Drive Mobile Robots in Low Traction Conditions with Slippage"],"prefix":"10.3390","volume":"13","author":[{"given":"Carlo","family":"De Giorgi","sequence":"first","affiliation":[{"name":"Department of Engineering for Innovation, University of Salento, Via per Monteroni, 73100 Lecce, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9070-0702","authenticated-orcid":false,"given":"Daniela","family":"De Palma","sequence":"additional","affiliation":[{"name":"Department of Engineering for Innovation, University of Salento, Via per Monteroni, 73100 Lecce, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4991-3698","authenticated-orcid":false,"given":"Gianfranco","family":"Parlangeli","sequence":"additional","affiliation":[{"name":"Department of Engineering for Innovation, University of Salento, Via per Monteroni, 73100 Lecce, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,27]]},"reference":[{"key":"ref_1","unstructured":"Borenstein, J., Everett, H., and Feng, L. (1996). Where Am I? Sensors and Methods for Mobile Robot Positioning, University of Michigan."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1109\/70.544770","article-title":"Measurement and correction of systematic odometry errors in mobile robots","volume":"12","author":"Borenstein","year":"1996","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sousa, R.B., Petry, M.R., and Moreira, A.P. (2020, January 15\u201317). Evolution of Odometry Calibration Methods for Ground Mobile Robots. Proceedings of the 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Ponta Delgada, Portugal.","DOI":"10.1109\/ICARSC49921.2020.9096154"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Goronzy, G., and Hellbrueck, H. (2017, January 21\u201325). Weighted online calibration for odometry of mobile robots. 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