Add AI_LIO_+

bib/AI_LIO_PLUS_Survey.bib

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+@article{TAO2026134317,
+title = {Generalized deep-learning LiDAR-camera fusion method of 3D object detection for autonomous driving: A survey},
+journal = {Neurocomputing},
+volume = {699},
+pages = {134317},
+year = {2026},
+issn = {0925-2312},
+doi = {https://doi.org/10.1016/j.neucom.2026.134317},
+url = {https://www.sciencedirect.com/science/article/pii/S0925231226017157},
+author = {Xianlu Tao and Zhuo Chen and Shuguo Pan and Wang Gao and Gaoyang Xiong and Feixuan Huang},
+keywords = {3D object detection, Deep learning, Multimodal, LiDAR, Camera, Autonomous driving},
+abstract = {With the rapid advancement of deep learning and increasing demands from autonomous driving, LiDAR–camera fusion-based 3D object detection (3DOD) has become a fundamental real-time perception task. The core challenge of such multimodal systems lies in mitigating modality discrepancies and effectively modeling cross-modal feature correlations. This paper presents a survey on LiDAR-camera fusion 3DOD in autonomous driving scenarios, with a focus on open-source autonomous driving datasets containing LiDAR-camera data in the real world, as well as the classification and development of LiDAR-camera fusion methods. Unlike prior surveys that focus on overall network architectures, we emphasize the implementation details of fusion strategies to enable a more thorough comparison. We further summarize a novel generalized fusion taxonomy, termed Representation-Augmented Fusion and Representation-Unified Fusion (RAF-RUF), to better reflect current research trends. In addition, fusion approaches involving other camera types and cooperative perception scenarios are briefly reviewed. Finally, open datasets and future research directions are discussed to facilitate practical deployment and further studies.}
+}

documentation/Benchmarks/AI_LIO+/README.md

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+# AI_LIO+ (no assignee)
+
+- https://www.sciencedirect.com/science/article/pii/S0925231226017157?via%3Dihub ([[BIB]](https://github.com/MapsHD/HDMapping/blob/main/bib/AI_LIO_PLUS_Survey.bib), no source code - survey article containing links to methods)