Simultaneous localization and mapping (SLAM) is the ability of a robot system to relocate itself within an unknown environment and build the map at same time, based on the sensor measurements and motion updating. The original SLAM work began from 1986, 32 years ago till now. Currently SLAM has became the core module in Autonomous Driving, last-mile delivery and Warehouse sorting robotics. Place recognition is the key module in the SLAM framework, which is used to find loop closure within the history trajectory and plays an important role in the map optimization and localization. But in real long-term navigation task, place recognition methods still face the challenges from the following three aspects: 1) Feature Extraction: is to extract unique feature descriptions for local scenes. Traditional traction extraction in SLAM methods may get affected by the illumination changes (from day to night, and abrupt light condition changing), thus can not provide stable features for long-term navigation task. On their other hand, appearance changed caused by seasons and weathers will also introduce measurement uncertainty in feature extraction; 2) Scene Matching: is to retrieval previous visited scenes based on the feature descriptions, and constructed a loop on the global map. Traditional SLAM methods rely on single-frame for LCD detecting, but this approach is unstable and will get external noise under variant environment conditions, when the robot system is moving fast or the environment has dynamic objects. In 2012, Milford et.al proposed a sequence matching base place recognition based, which is robust to environment conditions and dynamic objects. But since the computation complexity of sequence based matching method is higher than the single-frame based method. According to the previous analysis on place recognition, in this paper we aim to provide an efficiency and accurate place recognition method based on sequence matching for long-term navigation task. The main contributions of this paper are: ? Aim to learn robust feature in for local place description, we proposed an unsupervised feature learning method, which can learn the hidden features of scenes without human labeling. And the extracted feature is more robust to the environment condition changing and dynamic objects; For the sake of practical loop closure detection, we design an multi-resolution sampling based sequence matching method, which can improve the efficiency and accuracy of sequence matching greatly in the long-term navigation task; ? We design an multi-resolution sampling based sequential matching method in largescale place matching. Finally, we build a platform and relative software to gather the raw data in the cloud online for raining the robust feature and further algorithm evaluation requirements.