{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T23:16:56Z","timestamp":1791847016671,"version":"4.3.4"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"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":["Auton Robot"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s10514-023-10113-9","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T18:01:45Z","timestamp":1688493705000},"page":"695-714","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["TNES: terrain traversability mapping, navigation and excavation system for autonomous excavators on worksite"],"prefix":"10.1007","volume":"47","author":[{"given":"Tianrui","family":"Guan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenpeng","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruitao","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangjun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,4]]},"reference":[{"issue":"3","key":"10113_CR1","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1002\/rob.21657","volume":"34","author":"J Ahtiainen","year":"2017","unstructured":"Ahtiainen, J., Stoyanov, T., & Saarinen, J. (2017). Normal distributions transform traversability maps: Lidar-only approach for traversability mapping in outdoor environments. Journal of Field Robotics, 34(3), 600\u2013621. https:\/\/doi.org\/10.1002\/rob.21657","journal-title":"Journal of Field Robotics"},{"key":"10113_CR2","doi-asserted-by":"publisher","unstructured":"Bellone, M., Messina, A., & Reina, G. (2013). A new approach for terrain analysis in mobile robot applications. In 2013 IEEE international conference on mechatronics (ICM) (pp. 225\u2013230). https:\/\/doi.org\/10.1109\/ICMECH.2013.6518540","DOI":"10.1109\/ICMECH.2013.6518540"},{"key":"10113_CR3","doi-asserted-by":"publisher","DOI":"10.1108\/SR-03-2013-644","author":"M Bellone","year":"2014","unstructured":"Bellone, M., Reina, G., Giannoccaro, N., & Spedicato, L. (2014). 3D traversability awareness for rough terrain mobile robots. Sensor Review. https:\/\/doi.org\/10.1108\/SR-03-2013-644","journal-title":"Sensor Review"},{"key":"10113_CR4","doi-asserted-by":"crossref","unstructured":"Braun, T., Bitsch, H., & Berns, K. (2008). Visual terrain traversability estimation using a combined slope\/elevation model. In A. R. Dengel, K. Berns, T. M. Breuel, F. Bomarius, & T. R. Roth-Berghofer (Eds.), KI 2008: Advances in Artificial Intelligence (pp. 177\u2013184). Springer.","DOI":"10.1007\/978-3-540-85845-4_22"},{"issue":"3","key":"10113_CR5","doi-asserted-by":"publisher","first-page":"1695","DOI":"10.1109\/LRA.2018.2801794","volume":"3","author":"RO Chavez-Garcia","year":"2018","unstructured":"Chavez-Garcia, R. O., Guzzi, J., Gambardella, L. M., & Giusti, A. (2018). Learning ground traversability from simulations. IEEE Robotics and Automation Letters, 3(3), 1695\u20131702. https:\/\/doi.org\/10.1109\/LRA.2018.2801794","journal-title":"IEEE Robotics and Automation Letters"},{"key":"10113_CR6","doi-asserted-by":"publisher","unstructured":"Chilian, A., & Hirschm\u00fcller, H. (2009). Stereo camera based navigation of mobile robots on rough terrain. In 2009 IEEE\/RSJ international conference on intelligent robots and systems (pp. 4571\u20134576). https:\/\/doi.org\/10.1109\/IROS.2009.5354535","DOI":"10.1109\/IROS.2009.5354535"},{"key":"10113_CR7","doi-asserted-by":"crossref","unstructured":"Cortinhal, T., Tzelepis, G., & Aksoy, E. E. (2020). SalsaNext: Fast, uncertainty-aware semantic segmentation of LiDAR point clouds for autonomous driving.","DOI":"10.1007\/978-3-030-64559-5_16"},{"key":"10113_CR8","doi-asserted-by":"crossref","unstructured":"Dahlkamp, H., Kaehler, A., Stavens, D., Thrun, S., & Bradski, G. R. (2006). Self-supervised monocular road detection in desert terrain. In Robotics: Science and systems.","DOI":"10.15607\/RSS.2006.II.005"},{"key":"10113_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/s17092101","author":"F Deng","year":"2017","unstructured":"Deng, F., Zhu, X., & He, C. (2017). Vision-based real-time traversable region detection for mobile robot in the outdoors. Sensors. https:\/\/doi.org\/10.3390\/s17092101","journal-title":"Sensors"},{"issue":"3","key":"10113_CR10","doi-asserted-by":"publisher","first-page":"7083","DOI":"10.1109\/LRA.2022.3180439","volume":"7","author":"P Ewen","year":"2022","unstructured":"Ewen, P., Li, A., Chen, Y., Hong, S., & Vasudevan, R. (2022). These maps are made for walking: Real-time terrain property estimation for mobile robots. IEEE Robotics and Automation Letters, 7(3), 7083\u20137090.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"10113_CR11","doi-asserted-by":"crossref","unstructured":"Fankhauser, P., & Hutter, M. (2016). A universal grid map library: Implementation and use case for rough terrain navigation. In Robot operating system (ROS) (pp. 99\u2013120). Springer","DOI":"10.1007\/978-3-319-26054-9_5"},{"key":"10113_CR12","doi-asserted-by":"crossref","unstructured":"Frey, J., Hoeller, D., Khattak, S., & Hutter, M. (2022). Locomotion policy guided traversability learning using volumetric representations of complex environments. In 2022 IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp. 5722\u20135729 . IEEE","DOI":"10.1109\/IROS47612.2022.9982190"},{"issue":"11","key":"10113_CR13","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013). Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32(11), 1231\u20131237. https:\/\/doi.org\/10.1177\/0278364913491297","journal-title":"The International Journal of Robotics Research"},{"key":"10113_CR14","doi-asserted-by":"crossref","unstructured":"Guan, T., He, Z., Song, R., Manocha, D., & Zhang, L. (2021). TNS: Terrain traversability mapping and navigation system for autonomous excavators. In Robotics: Science and Systems XVIII.","DOI":"10.15607\/RSS.2022.XVIII.049"},{"key":"10113_CR15","doi-asserted-by":"crossref","unstructured":"Guan, T., Song, R., Ye, Z., & Zhang, L. (2023). Vinet: Visual and inertial-based terrain classification and adaptive navigation over unknown terrain. In 2023 IEEE international conference on robotics and automation (ICRA)","DOI":"10.1109\/ICRA48891.2023.10161251"},{"issue":"3","key":"10113_CR16","doi-asserted-by":"publisher","first-page":"8138","DOI":"10.1109\/LRA.2022.3187278","volume":"7","author":"T Guan","year":"2022","unstructured":"Guan, T., Kothandaraman, D., Chandra, R., Sathyamoorthy, A. J., Weerakoon, K., & Manocha, D. (2022). GA-Nav: Efficient terrain segmentation for robot navigation in unstructured outdoor environments. IEEE Robotics and Automation Letters, 7(3), 8138\u20138145. https:\/\/doi.org\/10.1109\/LRA.2022.3187278","journal-title":"IEEE Robotics and Automation Letters"},{"key":"10113_CR17","unstructured":"He, D., Xu, W., Zhang, F. (2021). Embedding manifold structures into Kalman filters. arXiv preprint arXiv:2102.03804"},{"issue":"5","key":"10113_CR18","doi-asserted-by":"publisher","first-page":"172988141773540","DOI":"10.1177\/1729881417735401","volume":"14","author":"RA Hewitt","year":"2017","unstructured":"Hewitt, R. A., Ellery, A., & de Ruiter, A. (2017). Training a terrain traversability classifier for a planetary rover through simulation. International Journal of Advanced Robotic Systems, 14(5), 1729881417735401. https:\/\/doi.org\/10.1177\/1729881417735401","journal-title":"International Journal of Advanced Robotic Systems"},{"key":"10113_CR19","doi-asserted-by":"publisher","unstructured":"Hirose, N., Sadeghian, A., V\u00e1zquez, M., Goebel, P., & Savarese, S. (2018). Gonet: A semi-supervised deep learning approach for traversability estimation. In 2018 IEEE\/RSJ international conference on intelligent robots and systems (IROS) (pp. 3044\u20133051). https:\/\/doi.org\/10.1109\/IROS.2018.8594031","DOI":"10.1109\/IROS.2018.8594031"},{"key":"10113_CR20","doi-asserted-by":"crossref","unstructured":"Hoffmann, G. M., Tomlin, C. J., Montemerlo, M., & Thrun, S. (2007). Autonomous automobile trajectory tracking for off-road driving: Controller design, experimental validation and racing. In 2007 American control conference (pp. 2296\u20132301). IEEE","DOI":"10.1109\/ACC.2007.4282788"},{"key":"10113_CR21","doi-asserted-by":"publisher","unstructured":"Holder, C. J., & Breckon, T. P. (2018). Learning to drive: Using visual odometry to bootstrap deep learning for off-road path prediction. In 2018 IEEE intelligent vehicles symposium (IV) (pp. 2104\u20132110). https:\/\/doi.org\/10.1109\/IVS.2018.8500526","DOI":"10.1109\/IVS.2018.8500526"},{"key":"10113_CR22","doi-asserted-by":"crossref","unstructured":"Jiang, P., Osteen, P. R., Wigness, M., Saripalli, S. (2021). Rellis-3d dataset: Data, benchmarks and analysis. In 2021 IEEE international conference on robotics and automation (ICRA) (pp. 1110\u20131116).","DOI":"10.1109\/ICRA48506.2021.9561251"},{"key":"10113_CR23","doi-asserted-by":"crossref","unstructured":"Julier, S. J., & Uhlmann, J. K. (1997). New extension of the Kalman filter to nonlinear systems. In Signal processing, sensor fusion, and target recognition VI (Vol. 3068, pp. 182\u2013193). Spie","DOI":"10.1117\/12.280797"},{"issue":"2","key":"10113_CR24","doi-asserted-by":"publisher","first-page":"1312","DOI":"10.1109\/LRA.2021.3057023","volume":"6","author":"G Kahn","year":"2021","unstructured":"Kahn, G., Abbeel, P., & Levine, S. (2021). Badgr: An autonomous self-supervised learning-based navigation system. IEEE Robotics and Automation Letters, 6(2), 1312\u20131319.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"10113_CR25","doi-asserted-by":"publisher","unstructured":"Khan, M. M., Ali, H., Berns, K., & Muhammad, A. (2016). Road traversability analysis using network properties of roadmaps. In 2016 IEEE\/RSJ international conference on intelligent robots and systems (IROS) (pp. 2960\u20132965). https:\/\/doi.org\/10.1109\/IROS.2016.7759458","DOI":"10.1109\/IROS.2016.7759458"},{"key":"10113_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41315-020-00148-x","volume":"4","author":"M Khan","year":"2020","unstructured":"Khan, M., Berns, K., & Muhammad, A. (2020). Vehicle specific robust traversability indices using roadmaps on 3d pointclouds. International Journal of Intelligent Robotics and Applications, 4, 1\u201317. https:\/\/doi.org\/10.1007\/s41315-020-00148-x","journal-title":"International Journal of Intelligent Robotics and Applications"},{"key":"10113_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0926-5805(02)00034-1","volume":"12","author":"S-K Kim","year":"2003","unstructured":"Kim, S.-K., & Russell, J. (2003). Framework for an intelligent earthwork system: Part I. System architecture. Automation in Construction, 12, 1\u201313.","journal-title":"Automation in Construction"},{"key":"10113_CR28","doi-asserted-by":"publisher","unstructured":"Kingry, N., Jung, M., Derse, E., Dai, R. (2018). Vision-based terrain classification and solar irradiance mapping for solar-powered robotics. In 2018 IEEE\/RSJ international conference on intelligent robots and systems (IROS) (pp. 5834\u20135840). https:\/\/doi.org\/10.1109\/IROS.2018.8593635","DOI":"10.1109\/IROS.2018.8593635"},{"key":"10113_CR29","doi-asserted-by":"crossref","unstructured":"Kumar, A., Fu, Z., Pathak, D., & Malik, J. (2021). RMA: Rapid motor adaptation for legged robots.","DOI":"10.15607\/RSS.2021.XVII.011"},{"key":"10113_CR30","unstructured":"Kurzer, K. (2016). Path planning in unstructured environments: A real-time hybrid A* implementation for fast and deterministic path generation for the kth research concept vehicle. Master\u2019s thesis"},{"issue":"4","key":"10113_CR31","doi-asserted-by":"publisher","first-page":"7469","DOI":"10.1109\/LRA.2021.3095515","volume":"6","author":"J Lin","year":"2021","unstructured":"Lin, J., Zheng, C., Xu, W., & Zhang, F. (2021). R2 live: A robust, real-time, lidar-inertial-visual tightly-coupled state estimator and mapping. IEEE Robotics and Automation Letters, 6(4), 7469\u20137476.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"10113_CR32","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1023\/B:AURO.0000047286.62481.1d","volume":"18","author":"R Manduchi","year":"2005","unstructured":"Manduchi, R., Castano, A., Talukder, A., & Matthies, L. (2005). Obstacle detection and terrain classification for autonomous off-road navigation. Autonomous Robots, 18, 81\u2013102. https:\/\/doi.org\/10.1023\/B:AURO.0000047286.62481.1d","journal-title":"Autonomous Robots"},{"issue":"3\u20134","key":"10113_CR33","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/s10846-017-0760-x","volume":"92","author":"S Matsuzaki","year":"2018","unstructured":"Matsuzaki, S., Yamazaki, K., Hara, Y., & Tsubouchi, T. (2018). Traversable region estimation for mobile robots in an outdoor image. Journal of Intelligent & Robotic Systems, 92(3\u20134), 453\u2013463. https:\/\/doi.org\/10.1007\/s10846-017-0760-x","journal-title":"Journal of Intelligent & Robotic Systems"},{"key":"10113_CR34","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/978-3-319-67361-5_22","volume-title":"Field and service robotics","author":"D Maturana","year":"2018","unstructured":"Maturana, D., Chou, P.-W., Uenoyama, M., & Scherer, S. (2018). Real-time semantic mapping for autonomous off-road navigation. In M. Hutter & R. Siegwart (Eds.), Field and service robotics (pp. 335\u2013350). Springer."},{"key":"10113_CR35","doi-asserted-by":"publisher","unstructured":"Nath, N. D., & Behzadan, A. H. (2020). Deep convolutional networks for construction object detection under different visual conditions. Frontiers in Built Environment, 6, 97. https:\/\/doi.org\/10.3389\/fbuil.2020.00097","DOI":"10.3389\/fbuil.2020.00097"},{"issue":"4","key":"10113_CR36","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1016\/j.engappai.2013.01.006","volume":"26","author":"P Papadakis","year":"2013","unstructured":"Papadakis, P. (2013). Terrain traversability analysis methods for unmanned ground vehicles: A survey. Engineering Applications of Artificial Intelligence, 26(4), 1373\u20131385. https:\/\/doi.org\/10.1016\/j.engappai.2013.01.006","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10113_CR37","doi-asserted-by":"publisher","unstructured":"Paz, D., Zhang, H., Li, Q., Xiang, H., & Christensen, H. I. (2020). Probabilistic semantic mapping for urban autonomous driving applications. In 2020 IEEE\/RSJ international conference on intelligent robots and systems (IROS) (pp. 2059\u20132064). https:\/\/doi.org\/10.1109\/IROS45743.2020.9341738","DOI":"10.1109\/IROS45743.2020.9341738"},{"key":"10113_CR38","unstructured":"Poudel, R. P. K., Liwicki, S., & Cipolla, R. (2019). Fast-SCNN: Fast semantic segmentation network. In BMVC."},{"issue":"2","key":"10113_CR39","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1002\/rob.20279","volume":"26","author":"MJ Procopio","year":"2009","unstructured":"Procopio, M. J., Mulligan, J., & Grudic, G. (2009). Learning terrain segmentation with classifier ensembles for autonomous robot navigation in unstructured environments. Journal of Field Robotics, 26(2), 145\u2013175. https:\/\/doi.org\/10.1002\/rob.20279","journal-title":"Journal of Field Robotics"},{"key":"10113_CR40","unstructured":"R\u00a0Shamshiri, R., Weltzien, C., Hameed, I. A., J\u00a0Yule, I., E\u00a0Grift, T., Balasundram, S. K., Pitonakova, L., Ahmad, D., & Chowdhary, G. (2018). Research and development in agricultural robotics: A perspective of digital farming"},{"key":"10113_CR41","doi-asserted-by":"crossref","unstructured":"Ranftl, R., Bochkovskiy, A., Koltun, V. (2021). Vision transformers for dense prediction. In ICCV","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"10113_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2019.04.006","author":"D Roberts","year":"2019","unstructured":"Roberts, D., & Golparvar-Fard, M. (2019). End-to-end vision-based detection, tracking and activity analysis of earthmoving equipment filmed at ground level. Automation in Construction. https:\/\/doi.org\/10.1016\/j.autcon.2019.04.006","journal-title":"Automation in Construction"},{"key":"10113_CR43","doi-asserted-by":"publisher","unstructured":"Rosenfeld, R. D., Restrepo, M. G., Gerard, W. H., Bruce, W. E., Branch, A. A., Lewin, G. C., & Bezzo, N. (2018). Unsupervised surface classification to enhance the control performance of a UGV. In 2018 systems and information engineering design symposium (SIEDS) (pp. 225\u2013230). https:\/\/doi.org\/10.1109\/SIEDS.2018.8374741","DOI":"10.1109\/SIEDS.2018.8374741"},{"key":"10113_CR44","doi-asserted-by":"crossref","unstructured":"Rothrock, B., Kennedy, R., Cunningham, C. T., Papon, J., Heverly, M., & Ono, M. (2016). Spoc: Deep learning-based terrain classification for mars rover missions.","DOI":"10.2514\/6.2016-5539"},{"key":"10113_CR45","doi-asserted-by":"publisher","unstructured":"Schilling, F., Chen, X., Folkesson, J., & Jensfelt, P. (2017). Geometric and visual terrain classification for autonomous mobile navigation. In 2017 IEEE\/RSJ international conference on intelligent robots and systems (IROS) (pp. 2678\u20132684). https:\/\/doi.org\/10.1109\/IROS.2017.8206092","DOI":"10.1109\/IROS.2017.8206092"},{"issue":"7","key":"10113_CR46","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1016\/j.autcon.2011.03.013","volume":"20","author":"J Seo","year":"2011","unstructured":"Seo, J., Lee, S., Kim, J., & Kim, S.-K. (2011). Task planner design for an automated excavation system. Automation in Construction, 20(7), 954\u2013966. https:\/\/doi.org\/10.1016\/j.autcon.2011.03.013","journal-title":"Automation in Construction"},{"key":"10113_CR47","doi-asserted-by":"crossref","unstructured":"Shan, T., Englot, B., Meyers, D., Wang, W., Ratti, C., & Rus, D. (2020). LIO-SAM: Tightly-coupled lidar inertial odometry via smoothing and mapping. In 2020 IEEE\/RSJ international conference on intelligent robots and systems (IROS) (pp. 5135\u20135142). IEEE","DOI":"10.1109\/IROS45743.2020.9341176"},{"key":"10113_CR48","unstructured":"Shariati, H., Yeraliyev, A., Terai, B., Tafazoli, S., & Ramezani, M. (2019). Towards autonomous mining via intelligent excavators. In CVPR Workshops."},{"key":"10113_CR49","unstructured":"Singh, A., Singh, K., & Sujit, P. B. (2021). OffRoadTranSeg: Semi-supervised segmentation using transformers on OffRoad environments."},{"key":"10113_CR50","doi-asserted-by":"publisher","unstructured":"Sock, J., Kim, J., Min, J., & Kwak, K. (2016). Probabilistic traversability map generation using 3d-lidar and camera. In 2016 IEEE international conference on robotics and automation (ICRA) (pp. 5631\u20135637). https:\/\/doi.org\/10.1109\/ICRA.2016.7487782","DOI":"10.1109\/ICRA.2016.7487782"},{"key":"10113_CR51","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B. & Vasudevan, V. (2020). Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 2446\u20132454).","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"10113_CR52","doi-asserted-by":"publisher","unstructured":"Suryamurthy, V., Raghavan, V. S., Laurenzi, A., Tsagarakis, N. G., & Kanoulas, D. (2019). Terrain segmentation and roughness estimation using RGB data: Path planning application on the Centauro robot. In 2019 IEEE-RAS 19th international conference on humanoid robots (Humanoids) (pp. 1\u20138). https:\/\/doi.org\/10.1109\/Humanoids43949.2019.9035009","DOI":"10.1109\/Humanoids43949.2019.9035009"},{"key":"10113_CR53","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C. R., Deschaud, J.-E., Marcotegui, B., Goulette, F., Guibas, L. J. (2019). KPConv: Flexible and deformable convolution for point clouds. In Proceedings of the IEEE international conference on computer vision.","DOI":"10.1109\/ICCV.2019.00651"},{"key":"10113_CR54","doi-asserted-by":"publisher","unstructured":"Viswanath, K., Singh, K., Jiang, P., Sujit, P. B., & Saripalli, S. (2021). Offseg: A semantic segmentation framework for off-road driving. In 2021 IEEE 17th international conference on automation science and engineering (CASE) (pp. 354\u2013359). https:\/\/doi.org\/10.1109\/CASE49439.2021.9551643","DOI":"10.1109\/CASE49439.2021.9551643"},{"key":"10113_CR55","doi-asserted-by":"publisher","unstructured":"Wermelinger, M., Fankhauser, P., Diethelm, R., Kr\u00fcsi, P., Siegwart, R., & Hutter, M. (2016). Navigation planning for legged robots in challenging terrain. In 2016 IEEE\/RSJ international conference on intelligent robots and systems (IROS) (pp. 1184\u20131189). https:\/\/doi.org\/10.1109\/IROS.2016.7759199","DOI":"10.1109\/IROS.2016.7759199"},{"key":"10113_CR56","doi-asserted-by":"crossref","unstructured":"Wigness, M., Eum, S., Rogers, J.G., Han, D., & Kwon, H. (2019). A rugd dataset for autonomous navigation and visual perception in unstructured outdoor environments. In International conference on intelligent robots and systems (IROS).","DOI":"10.1109\/IROS40897.2019.8968283"},{"key":"10113_CR57","unstructured":"Wu, H., Zhang, J., Huang, K., Liang, K., & Yizhou, Y. (2019). FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation."},{"key":"10113_CR58","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1109\/TIP.2020.3042065","volume":"30","author":"T Wu","year":"2021","unstructured":"Wu, T., Tang, S., Zhang, R., & Zhang, Y. (2021). Cgnet: A light-weight context guided network for semantic segmentation. IEEE Transactions on Image Processing, 30, 1169\u20131179.","journal-title":"IEEE Transactions on Image Processing"},{"key":"10113_CR59","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021). Segformer: Simple and efficient design for semantic segmentation with transformers. In Thirty-Fifth conference on neural information processing systems. https:\/\/openreview.net\/forum?id=OG18MI5TRL"},{"issue":"4","key":"10113_CR60","doi-asserted-by":"publisher","first-page":"2053","DOI":"10.1109\/TRO.2022.3141876","volume":"38","author":"W Xu","year":"2022","unstructured":"Xu, W., Cai, Y., He, D., Lin, J., & Zhang, F. (2022). Fast-lio2: Fast direct lidar-inertial odometry. IEEE Transactions on Robotics, 38(4), 2053\u20132073.","journal-title":"IEEE Transactions on Robotics"},{"key":"10113_CR61","doi-asserted-by":"crossref","unstructured":"Xue, J., Zhang, H., Dana, K., & Nishino, K. (2017). Differential angular imaging for material recognition. In 2017 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 6940\u20136949).","DOI":"10.1109\/CVPR.2017.734"},{"key":"10113_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-021-01515-2","volume":"129","author":"C Yu","year":"2021","unstructured":"Yu, C., Gao, C., Wang, J., Yu, G., Shen, C., & Sang, N. (2021). Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation. International Journal of Computer Vision, 129, 1\u201318. https:\/\/doi.org\/10.1007\/s11263-021-01515-2","journal-title":"International Journal of Computer Vision"},{"key":"10113_CR63","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abc3164","author":"L Zhang","year":"2021","unstructured":"Zhang, L., Zhao, J., Long, P., Wang, L., Qian, L., Lu, F., Song, X., & Manocha, D. (2021). An autonomous excavator system for material loading tasks. Science Robotics. https:\/\/doi.org\/10.1126\/scirobotics.abc3164","journal-title":"Science Robotics"},{"key":"10113_CR64","doi-asserted-by":"publisher","unstructured":"Zhao, Y., Liu, P., Xue, W., Miao, R., Gong, Z., & Ying, R. (2019). Semantic probabilistic traversable map generation for robot path planning. In 2019 IEEE international conference on robotics and biomimetics (ROBIO) (pp. 2576\u20132582). https:\/\/doi.org\/10.1109\/ROBIO49542.2019.8961533","DOI":"10.1109\/ROBIO49542.2019.8961533"},{"key":"10113_CR65","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H. S., & Zhang, L. (2021). Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In CVPR","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"10113_CR66","doi-asserted-by":"publisher","unstructured":"Zhou, Y., Huang, Y., & Xiong, Z. (2021). 3D traversability map generation for mobile robots based on point cloud. In 2021 IEEE\/ASME international conference on advanced intelligent mechatronics (AIM) (pp. 836\u2013841). https:\/\/doi.org\/10.1109\/AIM46487.2021.9517463","DOI":"10.1109\/AIM46487.2021.9517463"}],"container-title":["Autonomous Robots"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10514-023-10113-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10514-023-10113-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10514-023-10113-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T22:26:57Z","timestamp":1690496817000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10514-023-10113-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,4]]},"references-count":66,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["10113"],"URL":"https:\/\/doi.org\/10.1007\/s10514-023-10113-9","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2646589\/v1","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.3.rs-2646589\/v2","asserted-by":"object"}]},"ISSN":["0929-5593","1573-7527"],"issn-type":[{"value":"0929-5593","type":"print"},{"value":"1573-7527","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,4]]},"assertion":[{"value":"2 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No human participants and\/or animals involved.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights"}},{"value":"Informed consent is given to all authors of this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"This paper is an extension of the RSS 2022 submission TNS Guan et al. (\n                      \n                      ) and is invited to submit to Autonomous Robots.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"RSS special issue"}}]}}