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Automated Valet Parking (AVP) requires precise localization in challenginggarage conditions, including poor lighting, sparse textures, repetitivestructures, dynamic scenes, and the absence of Global Positioning System (GPS)signals, which often pose problems for conventional localization methods. Toaddress these adversities, we present AVM-SLAM, a semantic visual SLAMframework with multi-sensor fusion in a Bird's Eye View (BEV). Our frameworkintegrates four fisheye cameras, four wheel encoders, and an InertialMeasurement Unit (IMU). The fisheye cameras form an Around View Monitor (AVM)subsystem, generating BEV images. Convolutional Neural Networks (CNNs) extractsemantic features from these images, aiding in mapping and localization tasks.These semantic features provide long-term stability and perspective invariance,effectively mitigating environmental challenges. Additionally, data fusion fromwheel encoders and IMU enhances system robustness by improving motionestimation and reducing drift. To validate AVM-SLAM's efficacy and robustness,we provide a large-scale, high-resolution underground garage dataset, availableat https://github.com/yale-cv/avm-slam. This dataset enables researchers tofurther explore and assess AVM-SLAM in similar environments.

AVM-SLAM: Semantic Visual SLAM with Multi-Sensor Fusion in a Bird's Eye View for Automated Valet Parking