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LiDAR\u2010based SLAM outperforms visual\u2010SLAM, especially in low visibility and challenging lighting conditions. However, these systems still face challenges like scene degradation when dealing with feature\u2010deficient degenerate environments such as long corridors or tunnels. Traditional LiDAR SLAM algorithms primarily focus on the extraction of geometric features from the scene, with less utilization of visual information, for example, LiDAR\u2010generated reflectivity (also commonly referred to as intensity image) and depth imagery. In this study, we explore the potential of fusing both geometric and LiDAR\u2010generated image features into the SLAM system in various forms, aiming to enhance the system's adaptability in diverse environments and its robustness against environment degeneracy. We propose a new multifeature\u2010modality SLAM designed for robust real\u2010time localization and mapping in challenging environments. Our method enhances and extracts visual features from LiDAR\u2010generated images, which are then fused with geometric features through a holistic residual function for pose optimization. We also integrate a deep learning\u2010based object removal algorithm to reduce sensitivity to moving objects and sensor noise. This article conducts an in\u2010depth comparison of the proposed algorithm with several leading technologies in terms of scan matching accuracy, robustness, odometry, and mapping. The experimental results vividly showcase the superiority of our method in achieving high scan matching success rates and strong resilience against random outliers and Gaussian noise across various challenging scenarios, compared to the existing LiDAR SLAM methods that rely solely on geometric features. Extensive field experiments conducted on publicly available data sets, along with independently developed backpack\u2010based and robotic platforms, validated the robustness and accuracy of the proposed approach in both indoor and outdoor environments. In 3D mapping, we quantified the precision of 3D points by comparing point clouds collected by high\u2010precision Mobile Laser Scanning (MLS) and Terrestrial Laser Scanning (TLS). Our method outperforms in terms of absolute pose errors (APE) and point cloud matching quality. Based on the fitted Weibull distribution, the root mean square error (RMS) of point\u2010to\u2010plane distances improved by 20%. Additionally, ablation tests revealed the efficacy of different components within our system.<\/jats:p>","DOI":"10.1002\/rob.70026","type":"journal-article","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T09:52:23Z","timestamp":1756115543000},"page":"330-352","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Degeneracy\u2010Resistant LiDAR\u2010SLAM Algorithm Based on Geometric and Visual Features' Fusion"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6686-7389","authenticated-orcid":false,"given":"Daping","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Land Surveying and Geo\u2010Informatics The Hong Kong Polytechnic University Hung Hom China"}]},{"given":"Wenzhong","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo\u2010Informatics The Hong Kong Polytechnic University Hung Hom China"}]},{"given":"Shuyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo\u2010Informatics The Hong Kong Polytechnic University Hung Hom China"}]},{"given":"Mingyan","family":"Nie","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo\u2010Informatics The Hong Kong Polytechnic University Hung Hom China"}]},{"given":"Yitao","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo\u2010Informatics The Hong Kong Polytechnic University Hung Hom China"}]},{"given":"Qiru","family":"Zhong","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo\u2010Informatics The Hong Kong Polytechnic University Hung Hom China"}]},{"given":"Shengyu","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo\u2010Informatics The Hong Kong Polytechnic University Hung Hom China"}]},{"given":"Ameer Hamza","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo\u2010Informatics The Hong Kong Polytechnic University Hung Hom China"}]}],"member":"311","published-online":{"date-parts":[[2025,8,25]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2699686"},{"key":"e_1_2_8_3_1","first-page":"13126","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"Baur S. 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