Drone photogrammetry vs SLAM. Can state-of-the-art SLAM systems replace well-established photogrammetry solutions for aerial mapping?

In drone survey missions the choice of technology used to be between two well-established systems, photogrammetry, and LiDAR dependent systems. When choosing the technology you need to consider operational factors such as cost and complexity. With the recent development of computer systems, algorithms, and larger popularization of autonomous vehicles systems for Simultaneous Localization And Mapping (SLAM) reached a stage where they can be used in mapping solutions as a primary means for achieving fast and accurate results.

SLAM is not new, at least not brand new technology. Its origins could be tracked in the late ’60s when new optimization techniques were introduced. It was in the late 80’s however when these techniques were considered as a solution for localization of unmanned vehicles and robots, with the first pioneering practical implementation shown in the early 90s. After the 2000s the interest started to grow, and few commercial solutions appear for autonomous navigation most of them financed by DARPA. After this period with the rapid development of microcontrollers, new optimization techniques, new and faster algorithms for computer vision the SLAM systems became fast, cheap, and reliable enough to be used in commercial products such as passenger and industrial vehicles. Most of these systems use cameras, radars, lidars, and different sensors to get an accurate map of the 3d environment around them.

Example of how modern vehicle autopilot uses SLAM to get a map of its environment. Colored points are 3D points estimated by the SLAM algorithm.

Vision Dynamix developed a brand-new state-of-the-art technology that uses SLAM to get an accurate map of the environment which can be later georeferenced. The benefit of this solution is that the mapping process is incredibly fast the results are obtained up to 20 times faster than the existing photogrammetry solutions, the process is straightforward with less need of highly specialized personnel and the workflow is simple.

The solution that is based on this technology and that we provide is called Dynamix Mapper.

In this article, we’ll explore the ways photogrammetry and SLAM are quite different from each other, even if their three-dimensional (3D) outputs look similar. We’ll dig deeper into specific applications and how SLAM can provide exceptional results for most missions at a fraction of the cost and complexity of photogrammetry.

We will explore the same applications and make a comparison with the other popular mapping technology, LiDAR in a separate article.

How does photogrammetry work?

In photogrammetry, a drone captures a large number of high-resolution photos over an area. These images overlap such that the same point on the ground is visible in multiple photos and from different vantage points. In a similar way that the human brain uses information from both eyes to provide depth perception, photogrammetry uses these multiple vantage points in images to generate a 3D map.

The result: a high-resolution 3D reconstruction that contains not only elevation/height information, but also texture, shape, and color for every point on the map, enabling easier interpretation of the resulting 3D point cloud.

The drone is preferable to fly on a so-called grid pattern, so we have constant overlap. This overlap should be at least 70% between images.

How does Dynamix Mapper work?

Dynamix Mapper uses video instead of images. This means that while the drone is flying above the area of interest instead of images we record a video. By using video we don’t need to worry about the overlap because the video contains many images separated by small displacement. For example, if we make a video with 30 frames per second this means that we have 30 images in one second. Every next image has a very small displacement from the previous one.

This displacement is used by the Dynamix Mapper mapping engine to calculate the 3d position of every pixel. And since we have a lot of data we can use every next frame to improve the position of already calculated positions of the pixels.

Using this technique removes two problems compared with photogrammetry. We don’t need the overlap anymore since we video itself ensures overlap. And we don’t need preprogrammed grid pattern to fly by. We can manually fly the drone in any position and capture the objects or location of interest.

Dynamix Mapper uses the continuous approach of collecting data in the sense that the video is recorded continuously with no need for special image timing or flight patterns. The flying process is more similar to solutions using LiDAR

After the data is collected, it is processed by Dynamix Mapper. The result is a 3D point cloud. This point cloud is in the local coordinate system. If we want to obtain absolute accuracy we can use two available methods:

1. Georeference the point cloud using ground control points, same as we do in photogrammetry solutions, or

2. Use the drone logs from the same flight from which the video is taken and fuse that information with the 3D data. Please note that here the end accuracy will depend on the drone sensors and the drone itself. Regular commercial drones such as DJI Phantom or DJI Mavic can reach an accuracy of 50 to 80 cm, but if you use some RTK drone system you can reach an accuracy of up to a few cm.

Besides the point cloud, we also get the ortomozaic, elevation/height model, and textured mesh.

The most important thing the accuracy


In the case of photogrammetry, a quality, high-resolution, full-frame sensor camera can yield outputs with horizontal (x-y) accuracies in the range of 3 cm (1.2 in) and elevation (z) accuracies in the range of 4 to 8 cm (1.6 to 3.1 in) over hard surfaces, enabling precise volumetric analysis.

Note, however, that in order to achieve such performance the payload used for photogrammetry must be a professional one, with the right image sensor and lens to capture more detail. It’s not just about the number of pixels. In fact, two cameras with the same number of megapixels and different size sensors provide different image quality and accuracy.

This usually means that to achieve this accuracy you need a professional drone and camera solution.

Proper mission planning and post-processing are also important for achieving optimal accuracy: good overlap among images increases accuracy and provides better error correction compared to complete reliance on the direct geo-referencing method used in LIDAR. A high-end drone system with professional mission planning and post-processing workflow help ensure that you capture quality data that generates accurate results.

Dynamix Mapper

Dynamix Mapper can provide variable pixel density, depending on project needs and how fast we want to acquire the result. We use commercially available drones such as DJI Mavic 2 Pro and DJI Phantom 4 Pro. The cameras of these drones are suitable for horizontal accuracy in the range of 4 cm (1.6 in) and elevation accuracies in the range of 4 to 8 cm (1.6 to 3.1 in). Professional drones with professional cameras can achieve better accuracy.

The mission planning here is not that critical as mentioned in the photogrammetry. We don’t need separate drone apps where we can prepare the flight path and then upload this to the drone. We still have that option but we don’t rely on it since we can fly the drone manually and still get accurate results.

One important distinction is that in order to get the expected accuracy in photogrammetry you need to be able to map the full extent of the area. Meaning that even if one image is not correct for example it turned out blurry due to some environmental or technical factors the whole map is useless or in the best case scenario not as accurate and precise.

Dynamix mapper can map separate segments of the surveyed location with the same accuracy. Also, you can map only part of the area and not rely on the whole video of there is no need for that.



Both photogrammetry and Dynamix Mapper rely on drones to capture data. In these cases, you can expect to cover up to 5 to 10 km2 (2 – 4 mi2) per flight depending on the drone used.


Photogrammetry and Dynamix Mapper are both offer photorealistic mapping results in the form of orthomosaics, point clouds, and textured mesh. A true, life-like digital twin. This can be extremely helpful for identifying and measuring features, especially when the accuracy is so good.

Workflow and support

In the case of photogrammetry, companies like Pix4D, Agisoft, Bentley CC, Propeller and Dronedeploy have optimized workflows over years of experience with much data. In some cases, the workflow is turnkey—one software suite offers clients processing and accuracy verification within 24 hours when they simply upload the data. Cloud/server solutions allow for large-scale data processing with minimal hardware investment. And dedicated support teams are available to help.

However, this workflow is not always as straightforward as advertised and depends on the expertise of the operators, mapping setting, and parametrization of the mapping process and the mapping site itself. These solutions are providing options usually separated by specific features and needs. For example, the customer usually ends up with one product for mapping and another for processing and data analysis. Even with optimized workflow, this means more overhead and operational costs in day-to-day operations. The cloud-based solutions introduce more steps in the operation, most relevant is the need for uploading the images and waiting for the results. Since they operate on the cloud the customer can’t be sure when the results are ready, since the time of processing does not depend only on the project but on the number of users waiting for cloud pressing time.

Vision Dynamix with Dynamix Mapper provides a complete solution in the form of a desktop application that contains all necessary tools and features to complete the whole surveying project, from mapping to point cloud analyzing and exporting the result. The solution also works on a modest setup and does not require specialized hardware. Since the processing time, a much faster the initial mapping can be done on-site which removes the need of long waiting for the result.

One important feature that Dynamix Mapper has is that the mapping process is interactive meaning you can still interact with the application while it is mapping. Also, the mapping is done sequentially so the user can see the results on the already mapped segments with no need to wait for the whole process to finish. He can also stop the mapping at any time and continue from that point on. This is the benefit of using video and SLAM algorithms. There is also an option to map a segment selected from the images directly or by selecting a segment from the flight path.

Processing time

Photogrammetry processing for full resolution takes several hours (or days) depending on the project size. If you only need a sparse set of accurate tie points (like from a LIDAR source), photogrammetry tools offer down-sampled processing options.

Dynamix Mapper’s processing time also depends on the project size but it usually takes a few minutes. The processing times compared with photogrammetry are up to 20 times faster. It also offers down-sampled processing options in a sense of downsampling the original video or reducing the details in the mapping algorithm.

Mapping of the site with a 30ha area. The complete time is 21 minutes (lower right corner)


The difference between photogrammetry and Dynamix Mapper grows when considering operational and logistical factors. In order to generate quality results, a photogrammetry system requires all of its components to work perfectly in sync. Small changes in images or not enough overlap can lead to significant errors in outputs. Or worse, outputs that “look” right but are not. Most of the time, the only solution for erroneous photogrammetry data is to repeat flights.

Also, photogrammetry usually requires an expert who understands the workflow and details of each procedure and environmental factors that can affect the data. Therefore well-established companies such as the ones mentioned above have extensive training programs, webinars, and all-around support.

Dynamix Mapper is more forgiving and has a shorter learning curve. Vision Dynamix also provides training material in a form of tutorial videos and provides all-around support for its customers. The whole system has a shorter learning curve, the simplicity of the workflow and the fast-processing time makes it very flexible and cost-effective. The operation cost less in terms of personal and time spend on site and processing which makes it perfect for a wide range of uses from small projects where the cost is too high for other solutions to large projects where the speed of processing can bring higher profit.

Final thoughts

We have explored the differences between how photogrammetry and SLAM (thought Dynamix Mapper) work and the similarities in their outputs and learned about situations where each technology can be best applied. Bot technologies can meet most of the everyday challenges presented across a range of projects and industries, providing exceptional accuracy and stunningly detailed maps, available on demand and with minimal expertise overhead.

Photogrammetry is a well-established technology for aerial mapping. There are more than a few companies that are providing these types of solutions. They also have a well-established workflow that covers most of the projects.

However, we are in a time and age of constant improvement. Photogrammetry has reached its peak and its performance can’t be improved much more. The time of new technologies is here, SLAM is one of them. The initial results from the mapping and 3d reconstruction are excellent and can match photogrammetry in every point, not to mention the speed of processing, simplified workflow, and interactive mapping process.

Dynamix Mapper by Vision Dynamix is the first product on the market that offers these benefits. So if you need a professional, cost-effective solution with less complexity that overpasses photogrammetry try out Dynamix Mapper.