Close Range Architectural Photogrammetric Modeling for GIS By Luke Zhou, SULI Intern at SLAC National Accelerator Laboratory

Photogrammetry Obtain geometric properties from images Size Shape Position So if the overall goal of this project was to determine if the use of photogrammetry at SLAC is a feasible option, I would suppose that it’d help to first explain what exactly photogrammetry is then. In short, it’s essentially the act of measuring the geometric properties of objects based on photographs of the said object rather than the object itself. That is, you can theoretically just place an object in front of you and take a few pictures of the object as you walk around it. Then, by some method, you link those pictures together by their overlapping similarities, thereby directly creating a 3D model. From this model, you can then determine the size, shape and position of the original object rather than measuring the object itself. This gives many advantages over a traditional methods of measuring by hand with a ruler or tape measure. A primary reason would be the direct production of a 3D model from the photographic images using some photogrammetric software. The alternative would be to hand measure all of the tiny details and then use a CAD program to make a model of those specific dimension. Photogrammetry takes out some of the tediousness by directly producing a 3D model by simply marking features on the images. No measurement is necessary.

Uses at SLAC Alignment GIS queries Magnets Beamlines Building info Basemap So how would we use photogrammetry at SLAC? Well, one of the main uses of photogrammetry is that it could theoretically serve as an alternative survey technique that could be used to align components such as magnets and beamlines in LCLS and SSRL. Compared to other surveying techniques, such as laser scanning, photogrammetry takes much less time to collect data. All that is truly needed is a few pictures.This would be especially helpful in areas such as LCLS where tunnel access is limited due to space, time or radiation. Photogrammetry can a quick way of getting to about a hundred microns of precision. In my project though, I’ve been looking at somewhat larger scale of use for photogrammetry. That is, I’ve studying the feasibility of using photogrammetry as an method of measuring the exterior layouts of buildings to add to SLAC’s geographic information system, or GIS. This GIS has a variety of uses as SLAC moves into the future. For example, in the metrology department that I work in, it could be used to answer various queries like the location of various magnets that have been moved or aligned at a particular time. Or outside of the department, it could be developed to access various information about utilities and buildings or used as an aid to emergency response procedures. Photogrammetry has several advantages here since the dimensions of most buildings are too large to measure without ladder or scaffolding use.

Survey Procedure Targets placed Images are taken Model generated in Australis A photogrammetric survey on any building is carried out in three basic steps. Targets are placed on and around the building. A series of photographs are taken The model is generated.

Targets Targets placed Images are taken Model generated in Australis This first step can actually be skipped if so desired, though it does help with the accuracy and ease of the later modeling process. Basically what this step entails is that you place small red, round, reflective targets on and around the building so that their centroids are easily visible and can be accurately recognized by the photogrammetric software. There are four types shown on the right: These singular targets are spread around to provide density to the target network. These constitute the majority of the links between individual photos. These coded targets have a unique pattern to them that allows them to be automatically linked to identical codes in other images. They would be used entirely in place of the single targets, though the number of coded targets available is severely limited. These rotating targets are placed in front of the building. They can be turned around so they continue to be viewable from any angle without moving their centroid. This is necessary to model corners. Otherwise, it is difficult to conclude whether or not a wall simply continues on or if a corner actually exists there are some higher ones too placed on camera stands. Finally, we have this scale bar that’s exactly 30 inches long that simply provides the model with a proper set of dimensions. The next step is to walk around the building, taking overlapping photos of it. More overlap is generally better, though it may end up being more work later on. The final step is to take all of these images and run them through a photogrammetric program. In this case, that’s Australis. What it does is find all of the targets and link them between pictures to create a 3D network of targets.

Targets Targets placed Images are taken Model generated in Australis The next step is to walk around the building, taking overlapping photos of it. More overlap is generally better, though it may end up being more work later on. Major building features need to be seen at least 3-4 times to accurately pinpoint in a photogrammetric model.

Australis Targets placed Images are taken Model generated in Australis

Australis Once you’ve taken a ring of overlapping photographs around the building, you can move onto the modeling part in a photogrammetry program called Australis. Australis scans each image for the bright, circular, red targets and attempts to find similar targets in the subsequent images. It is best able to do this with the coded targets since Australis is already programmed to recognize their patterns. The high target density on the codes also gives some data about the orientation of the camera position in the image relative to any other image the identical code appears in.

Target Network Eventually, a full 3D network of targets can be found. Albeit, it was not done as automatically as was hoped. Australis was really only able to map out the first wall and could not automatically recognize the sharp 90 degree corners. However, despite this, I still was able to generate this network fairly rapidly and accurately.

Wireframe Model The features of a building can also be marked. Such features include the corners, doors and windows. This is done in a similar fashion to how the original reflective targets were marked. The locations of these points will, in general, be accurately triangulated within the network created by the reflective targets. And then, you can get this wireframe model. It’s worthwhile to note that I only have two walls shown up there. I assure you that it was not because I was lazy or really ran out of time. Rather, this is close to about where I reached the limit of Australis. If I try to go much further than this, my screen starts look like this..

Limits of Australis You don’t really have to be familiar with Australis to know that having that many red X’s suddenly come up on the screen is not a good sign. The funny thing is that the wall shown here is the still the most stable of the 4. The most likely cause of this self-destruction in the model is the small errors associated with the marking of targets. It’s not generally a terrible issue with the reflective targets since they have a centroid that is easily pinpointed. The building features however are marked mostly by my own judgment. By zooming in, I can get within a few parts of an inch, however, as I keep marking points, the errors from each individual point accumulate. Eventually, Australis is simply unable to account for all of the error in hundreds of points. Australis is simply unable to calculate their appropriate positions relative to all of the other points and the model falls apart.

Feasibility Results Not what was hoped for, but salvagable Accurate partial models Future Steps: Corner Recognition Full Model Generation