Place recognition remains a challenging problem under various perceptual conditions, e.g., all weather, times of day, seasons, and viewpoint shifts. Different from most of the existing place recognition methods using pure vision, this article studies light detection and ranging (LiDAR) based approaches. Point clouds have some benefits for place recognition since they do not suffer from illumination changes. On the other hand, they are dramatically affected by structural changes from different viewpoints or across seasons. In this article, a novel LiDAR-based place recognition system is proposed to achieve long-term robust localization, even under severe seasonal changes and viewpoint shifts. To improve the efficiency, a compact cylindrical image model is designed to convert three-dimensional point clouds to two-dimensional images representing the prominent geometric relationships of scenes. The contexts (buildings, trees, road structures, etc.) of scenes are utilized for efficient place recognition. A sequence-based temporal consistency check is also introduced for postverification. Extensive real experiments on three datasets (Oxford RobotCar [1], NCLT [2], and DUT-AS) show that the proposed system outperforms both state-of-the-art visual and LiDAR-based methods, verifying its robust performance in challenging scenarios.