VI_MCPR: Viewpoint Invariant Place Recognition Driven by Multicamera for Large-Scale Environments

Place recognition (PR) is a critical component of simultaneous localization and mapping in the fields of autonomous driving and robotics. In outdoor large-scale and complex environments, existing vision-based place recognition (VPR) methods typically rely on single-camera input, which is inherently limited by its restricted field of view and, thus, vulnerable to viewpoint variations. To effectively fill the aforementioned drawbacks, we propose VI_MCPR, a novel method that supports input from any number of cameras. This method utilizes a multibranch, weight-sharing encoder structure to encode image features from multiperspective simultaneously. The robust feature attention pooling block is then utilized to learn high-order nonlinear features and latent correlations between features, effectively mitigating the loss of key features during down-sampling. To generate a discriminative global descriptor representing the image, we designed a geometry and spatial relationship enhanced block, named graph-SE-transform (GSET), which captures the overall shape of objects in a manner similar to the human visual system. Extensive comparative experiments on the NuScenes, Argoverse 2 Sensors, and real-vehicle datasets demonstrate that VI_MCPR outperforms state-of-the-art VPR methods. Compared to the strongest representative baselines, our approach increases PR performance by approximately 6% under viewpoint variations, by approximately 6% in dynamic environments, and by approximately 8% in extreme scenarios such as adverse weather, illumination changes, and low-texture conditions.