Simultaneous Localization and Mapping (SLAM) datasets are essential for evaluating SLAM algorithms, since selecting an accurate and reliable method is critical to the autonomous operation of unmanned systems. However, existing SLAM datasets focus primarily on scene diversity and lack a systematic evaluation of differing motion patterns—such as variations in speed and abrupt maneuvers. In real-world applications (e.g., field exploration or emergency response), unmanned ground vehicles (UGVs) often encounter these complex motions, which can lead to SLAM failures. To address this shortcoming, we have constructed the M2PT Dataset: a Multi-Motion-Pattern dataset for UGVs operating across varied Terrains. This dataset comprises LiDAR, inertial measurement unit (IMU), and wheel-odometry data recorded in three environments under four distinct motion patterns (low speed, high speed, sharp turns, and collisions), and provides high-precision GPS trajectory ground truth alongside map ground truth acquired by scanning devices. We compare the performance of several SLAM algorithms on this dataset to highlight its challenges and demonstrate its significance. The dataset can be accessed at https://drive.google.com/drive/folders/1_yRVQLY7cjDKHoFnMSsYqRr6ojSF6FGy .