Mobile robots have increasingly been gaining recognition in monitoring and inspection of civil infrastructure, owing to their potential to improve efficiency and accuracy. Specifically, quadruped robots offer enhanced stability and adaptability, rendering the robots ideal candidates for automated monitoring and inspection. To enable quadruped robots to conduct monitoring and inspection of civil infrastructure, the robots must be capable of autonomous navigation. Existing approaches towards autonomous navigation usually incorporate joint-state information to plan motions with whole-body controllers, representing a non-linear, high-dimensional problem that is computationally expensive to solve and is a particular burden when implemented into the robots for real-time navigation. This paper presents an integrated architecture for automated monitoring and inspection that consists (i) of a mission planning framework devised for planning monitoring and inspection tasks and (ii) a robust motion planning framework to enable real-time navigation. Specifically, the motion planning problem is decoupled into high-level task-space and low-level joint-space components, and the motion planning framework, building upon the “Cartographer” simultaneous localization and mapping algorithm, combines the “batch-informed trees” and A* algorithm for global planning and the “timed elastic band” for local planning and obstacle avoidance. For validation, the integrated architecture for automated monitoring and inspection is implemented into quadruped robots, and validation tests are conducted in indoor office environments to be inspected. As a result, it is demonstrated that the quadruped robots navigate safely and collision-free in real time, accommodating both static and dynamic obstacles. Enhancing the efficiency and accuracy of autonomous navigation of quadruped robots in complex and dynamic environments, the architecture is expected to pave the way for future research in robust controller development and 3D-state space planning.