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In this paper, we consider a team of mobile robots that are tasked with building a map of the obstacles, including occluded ones, in a given environment. We propose an integrated framework for mapping with see-through capabilities using laser and wireless channel measurements, which can provide mapping capabilities beyond existing methods in the literature. Our approach leverages the laser measurements to map the visible parts of the environment (the parts that can be sensed directly by the laser scanners) using occupancy grid mapping. The parts that cannot be properly mapped by laser scanners (e.g., the occluded parts) are then identified and mapped based on wireless channel measurements. For the latter, we extend our recently-proposed wireless-based obstacle mapping framework to a probabilistic approach using Bayesian Compressive Sensing. We further consider an integrated approach based on using total variation minimization. We compare the performance of our two integrated methods, using both simulated and real data, and show the underlying tradeoffs. Finally, we propose an adaptive path planning strategy that uses the current estimate of uncertainty to collect wireless measurements that are more informative for obstacle mapping. Overall, our framework enables mapping occluded structures that cannot be mapped with laser scanners alone or a small number of wireless measurements. Our experimental robotic testbed further confirms that the proposed integrated framework can map a more complex real occluded structure that cannot be mapped with existing strategies in the literature.