  Accurate localization in challenging garage environments -- marked by poor
lighting, sparse textures, repetitive structures, dynamic scenes, and the
absence of GPS -- is crucial for automated valet parking (AVP) tasks.
Addressing these challenges, our research introduces AVM-SLAM, a cutting-edge
semantic visual SLAM architecture with multi-sensor fusion in a bird's eye view
(BEV). This novel framework synergizes the capabilities of four fisheye
cameras, wheel encoders, and an inertial measurement unit (IMU) to construct a
robust SLAM system. Unique to our approach is the implementation of a flare
removal technique within the BEV imagery, significantly enhancing road marking
detection and semantic feature extraction by convolutional neural networks for
superior mapping and localization. Our work also pioneers a semantic
pre-qualification (SPQ) module, designed to adeptly handle the challenges posed
by environments with repetitive textures, thereby enhancing loop detection and
system robustness. To demonstrate the effectiveness and resilience of AVM-SLAM,
we have released a specialized multi-sensor and high-resolution dataset of an
underground garage, accessible at https://yale-cv.github.io/avm-slam_dataset,
encouraging further exploration and validation of our approach within similar
settings.
