Efficient Place Recognition With Complex Number Framework for Robust Environmental Description and Real-Time Performance

The rapid development of autonomous driving and robotics has led to increasingly higher demands for high-precision localization, making it an indispensable key component of modern autonomous systems. High-definition maps have become widely used to enhance localization accuracy, particularly in complex and dynamic environments. However, persistent challenges such as sensor measurement errors, environmental drift, and inaccuracies remain, making place recognition an increasingly critical research focus. This paper introduces a novel place recognition method based on a complex number framework, wherein environmental features are stored in both the real and imaginary parts of a complex number. This innovative approach significantly improves the richness and accuracy of environmental descriptions, thereby enhancing the system’s ability to distinguish between highly similar environments. Furthermore, a descriptor similarity evaluation strategy based on the angle between complex number vectors is proposed, which enhances the distinction of descriptors, especially in scenarios where the environments share similar characteristics, thus reducing the likelihood of mismatches. To ensure real-time performance, a hierarchical retrieval filtering process is introduced that effectively combines both coarse and fine-grained search strategies, optimizing the KD-Tree search and filtering out irrelevant matches. Experimental results on public datasets and real-world vehicle data across diverse scenarios validate the proposed method’s effectiveness and robustness, showing notable gains in recognition accuracy and real-time performance in complex environments.