In autonomous driving, place recognition is an essential component for successful mission execution, where Simultaneous Localization and Mapping (SLAM) is widely used in the system to estimate the vehicle's position by avoiding error accumulation. Various research efforts based on deep learning or handcrafted methods are currently underway to improve the place recognition performance. However, deep learning-based approaches pose certain challenges compared to handcrafted methods such as being less interpretable and often requiring extensive training time. In this paper, we propose a handcrafted method-based efficient feature extraction method for place recognition and point registration that aims to overcome these challenges while achieving high performance. The proposed method involves extracting robust feature points, generating powerful descriptors, and achieving performance levels similar to deep learning methods, while ensuring algorithmic versatility. Validation results using the KITTI dataset are also included in the paper, demonstrating exceptional performance even in demanding scenarios involving rotated loops. The results show an average loop closure accuracy of 95% or better, and an average pose estimation accuracy of 0.1 meters for translation and 0.15 degrees for rotation.