Reconstructing complex indoor environments with arbitrary wall orientations (original) (raw)

RECONSTRUCTING VIRTUAL ROOMS FROM PANORAMIC IMAGES

2005

arrangement from this flat image. For this reason, we present and discuss various visualizations of panoramic images. Furthermore, we propose an alternative visualization for panoramic images, using a simple 3-D recon- struction of the recording environment. Specifically, we reconstruct the 3-D layout of rectangular rooms from a panoramic image. By projecting the panoramic image onto the walls of this virtual room, the scene can be visu- alized as a 3-D model. The model can be shown either as a scene overview, or it can be displayed with the virtual camera at the position of the original camera, providing realistic views from within the room.

SEMI-AUTOMATIC FLOOR-PLAN RECONSTRUCTION FROM A 360 O PANORAMIC IMAGE

Abstract: The ease of panorama creation has made it very popular. Although it is a very convenient way to convey the environment, panoramic images can often be confusing. This discomfort has major influence in a 360 degree indoor panorama, where the viewer is forced to look in all the directions at the same time. In this paper we propose an alternative approach for visualization of the indoor environment. Instead of using the panorama directly, our method reconstructs a floor-plan from it and displays the created 3D model.

Exploration of Indoor Environments Predicting the Layout of Partially Observed Rooms

2020

We consider exploration tasks in which an autonomous mobile robot incrementally builds maps of initially unknown indoor environments. In such tasks, the robot makes a sequence of decisions on where to move next that, usually, are based on knowledge about the observed parts of the environment. In this paper, we present an approach that exploits a prediction of the geometric structure of the unknown parts of an environment to improve exploration performance. In particular, we leverage an existing method that reconstructs the layout of an environment starting from a partial grid map and that predicts the shape of partially observed rooms on the basis of geometric features representing the regularities of the indoor environment. Then, we originally employ the predicted layout to estimate the amount of new area the robot would observe from candidate locations in order to inform the selection of the next best location and to early stop the exploration when no further relevant area is expe...