A robust visual place recognition (VPR) is one of the significant requirements for visual simultaneous localization and mapping (SLAM). Many researchers have put forth their efforts for developing a robust VPR system that is invariant to changing environmental conditions while ensuring computational and memory efficiency. However, the development of such a system is still a challenge. Vision-based place recognition is highly dependent on the feature extraction and matching algorithms. Therefore, this research primarily focuses the process of robust feature selection and matching for accurate place recognition in challenging environments where appearance of a place drastically changes due to conditional variations, and highlights the limitations of the existing research. In this paper, a fast and affective visual place recognition method is presented that integrates the Bag-of-Words (BoW) vocabulary with robust feature matcher. The BoW reduces the search space for the image matching process by generating the match candidates while the robust feature matcher enhances the place matching performance by identifying and removing the spatially inconsistent feature matches on image plane between current frame and candidate image. The proposed method is evaluated on range of challenging benchmark datasets against state-of-the-art VPR methods. Through experiments the effectiveness of the proposed method is presented. Furthermore, this research elucidates the dilemma: which feature extraction algorithm performs better in a specific type of environment through evaluation of different feature detector-descriptor combinations on publicly available datasets.