With the current transition towards renewable and high-tech solutions, the world is becoming increasingly complex. Consequently, the challenges faced by firefighters also intensify. For that reason, firefighting robots are rising in popularity despite being far from perfect. An important area of improvement is the perception capabilities of those robots, given the fact that firefighting robots suffer from occluded camera views in environments filled with smoke. To overcome this challenge a LiDAR sensor may be used but experiments in this work show that even those point clouds are adversely affected by smoke. Consequently, this work presents a method for real-time reconstruction of ground surfaces in occluded environments filled with smoke. The developed method functions in ROS Noetic and merges segmented ground points, when available, with ground surfaces which are reconstructed based on information from segmented wall points. In this way, the method works even without the presence of ground points. To achieve this, a combination of established techniques from scientific literature, along with newly developed techniques were implemented. Doing so gives the robot’s operator an improved representation of the ground surface within environments filled with smoke. Ultimately the developed method may allow for autonomous navigation based on LiDAR data within environments filled with smoke. This research shows that a method consisting of techniques which tackle the independent sub-challenges arising from the use of LiDAR in indoor environments filled with smoke can effectively reconstruct the ground surfaces within those environments. Furthermore, the developed method has the potential to do so in a real-time manner.