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People in unfamiliar environments often need navigation guidance to reach a destination.\udResearch has found that compared to outdoors, people tend to lose orientation much more easily\udwithin complex buildings, such as university buildings and hospitals. This paper proposes\uda category-based method to generate landmark-based route instructions to support people's\udwayfinding activities in unfamiliar indoor environments. Compared to other methods relying\udon detailed instance-level data about the visual, semantic, and structural characteristics of individual\udspatial objects, the proposed method relies on commonly available data about categories of spatial\udobjects, which exist in most indoor spatial databases. With this, instructions like "Turn right after the\udsecond door, and use the elevator to go to the second floor" can be generated for indoor navigation. A case\udstudy with a university campus shows that the method is feasible in generating landmark-based\udroute instructions for indoor navigation. More importantly, compared to metric-based instructions\ud(i.e., the benchmark for indoor navigation), the generated landmark-based instructions can help users\udto unambiguously identify the correct decision point where a change of direction is needed, as well\udas offer information for the users to confirm that they are on the right way to the destination

“Turn Left after the WC, and Use the Lift to Go to the 2nd Floor”—Generation of Landmark-Based Route Instructions for Indoor Navigation