Visual place recognition (VPR) enables autonomous robots to identify previously visited locations, which contributes to tasks like simultaneous localization and mapping (SLAM). VPR faces challenges such as accurate image neighbor retrieval and appearance change in scenery. Event cameras, also known as dynamic vision sensors, are a new sensor modality for VPR and offer a promising solution to the challenges with their unique attributes: high temporal resolution (1MHz clock), ultra-low latency (in μs), and high dynamic range (>120dB). These attributes make event cameras less susceptible to motion blur and more robust in variable lighting conditions, making them suitable for addressing VPR challenges. However, the scarcity of event-based VPR datasets, partly due to the novelty and cost of event cameras, hampers their adoption. To fill this data gap, our paper introduces the NYC-Event-VPR dataset to the robotics and computer vision communities, featuring the Prophesee IMX636 HD event sensor (1280x720 resolution), combined with RGB camera and GPS module. It encompasses over 13 hours of geotagged event data, spanning 260 kilometers across New York City, covering diverse lighting and weather conditions, day/night scenarios, and multiple visits to various locations. Furthermore, our paper employs three frameworks to conduct generalization performance assessments, promoting innovation in event-based VPR and its integration into robotics applications.

Through Internet of Things (IoT) communication technology, collaborative perception enhances a vehicle’s capacity to discern its surroundings while driving by integrating and synchronizing sensor data from multiple agents. With the advancement of cooperative perception techniques in single-modality methods, there has been a growing trend toward integrating multimodal data from heterogeneous sensors in recent years. However, due to the data heterogeneity inherent in diverse sensors, Bird’s Eye View (BEV) maps generated from different types of sensors may exhibit local discrepancies in the spatial representation of entity positions. Furthermore, individual agents may produce uncertain and flawed feature representations in real noisy environments. The influence of this indeterminacy exacerbates the issue of local inconsistency, leading to misalignment of the detected target during BEV alignment and fusion, thereby reducing detection accuracy. To address these problems, we propose a modal decision-making spatial alignment cooperative perception network (MDNet). First, the network generates BEV feature maps through dense depth image supervision for voxel feature extraction and model-guided selective feature fusion. Subsequently, we achieve enhanced accuracy in object detection by performing spatial alignment of BEV representations generated from two distinct sensors, both globally and locally within the spatial domain. Besides, we employ a cascaded centralized pyramid strategy during the message fusion stage, facilitating flexible sampling across horizontal and vertical spatial dimensions, promoting deep interaction among multiple agents. We conduct quantitative and qualitative experiments on the public OPV2V and DAIR-V2X-C benchmarks, and our proposed MDNet exhibits superior performance and stronger robustness in the 3-D object detection task, providing more precise target detection results.