In general, the obstacle detection systems mainly rely on depth cameras or AI-based vision approaches; however, these methods are often constrained by limited fields of view and the need for continuous model retraining to adapt to complex and dynamic industrial scenes. To overcome these limitations, this study proposes a LiDAR-based obstacle detection and field monitoring system integrated with a quadruped robot. The proposed system focuses on three main components: real-time field mapping, intelligent path planning with obstacle avoidance, and field change detection. LiDAR point cloud data are pre-processed using pass-through and voxel grid filters, followed by coordinate transformation into the robot reference frame. The Cartographer simultaneous localization and mapping (SLAM) algorithm are employed to generate high-resolution occupancy grid maps for navigation. For autonomous operation, erosion processing and connected component labelling are used to define safe regions, while the A* algorithm enables efficient path planning and adaptive obstacle avoidance in complex environments. To detect unknown obstacles and environmental changes, Gaussian filtering and map differencing are applied, and map similarity is evaluated using histogram analysis and SIFT-based feature matching. Experimental results demonstrated that the system achieves a mapping resolution of 0.05 m and satisfies the Taiwan Association of Information and Communication Standards (TAICS) requirements, including 0.2 m planimetric accuracy and less than 0.1 m positional error. The proposed approach effectively identifies unknown obstacles and visually highlights risk areas, providing a reliable solution for intelligent workplace safety monitoring.