Impact of Moving Objects

Sometimes when scanning, the surrounding environment can be busy. For example, people passing close to the device whilst a scan is in progress or people opening doors; this could be due to security restrictions on the scanning site or cars passing by during outdoor scans. Many objects can move within the environment that is being scanned, which often cannot be prevented. The following section describes the impact of this type of movement on the device and the data quality attained.

SLAM Performance

Simultaneous Localization And Mapping (SLAM) is an algorithm that allows the device to identify its location and orientation within an environment. From the location and orientation, a trajectory can be built to which scan and image data is added, which enables the construction of a final point cloud. To process everything successfully, the device must be exposed to a sufficient amount of uniquely structured objects in its environment. The algorithm can identify its current position compared to its previous position using the surface shapes and corners of these objects.

Moving objects adds a specific difficulty for such systems; from the viewpoint of the device, it is unclear what is moving within the scene - the object or the device? The estimated location of the device will be incorrect and will be added incorrectly to the current dataset.

If a single object moves within a scene, the SLAM algorithm will probably be able to track its own location, especially if there are other objects around which do not move and are uniquely structured. However, moving objects within the following situations will lead to SLAM drifts or breaks, which can be seen as data overlaps, double walls or shortened/extended corridors:

• Featureless environments (e.g. tunnels, corridors, open fields or streets).

• Scenes with repeating structures (e.g. forests, fences, repetitive building outlines).

• More moving than fixed structures, like a door being opened in front of the scanner.

Examples for this can be the operator passing:

• People in a corridor.

• Moving robots along a fenced production line.

• Bushes and trees on one side and a car driving by on the other.

If SLAM breaks in such situations, this often cannot be corrected in post-processing. Instead, datasets need to be cut to exclude difficult scenes and aligned manually after separately post-processing each part. It is therefore advised to include considerations on moving objects during project planning and inform all stakeholders and contributors on the impact to the scan.

Effects on the SLAM algorithms will be experienced even if the scanning is paused since SLAM continues to run on it to localize the device.

Point Cloud Noise

NavVis has developed filter algorithms, which eliminate noise from moving objects as long as these objects keep moving continuously. However, a moving object, which stops moving within the scan image, will be present in the data. This might be a person passing by and then standing still to watch the operator scan, a self-closing door being opened and then kept open with the foot or hand, or a car slowing down for a pedestrian.

These semi-moving objects will leave traces in the processed point cloud, most often as noise hovering in the air in an otherwise open space. In NavVis quality maps, people moving often resemble cloudy white areas within the blue coloring without specific outlines.

If necessary, such noise can be cleaned in third party tools from the point cloud.

Image Quality

Moving objects within a scene will always be captured in the panoramas triggered at that point in time. Motion blur can likely be seen on such objects within the images, even if the operator with the device stands very still while capturing the panorama. Additionally, these moving objects will of course hide fixed objects within the scene which might be of interest in the scan.

Images that show unwanted moving objects can be hidden by IVION when necessary.

Point Cloud Coloring

Once moving objects are captured in images (and therefore panoramas), these will also be used for coloring the point cloud. Generally, in each point of the point cloud, the closest panorama will be selected, and the colors of objects will be transferred onto the correct point. If this closest location includes a moving object, its colors will be projected onto the fixed points behind it. This can most often be seen as people or parts of them being projected onto floors or walls, or fuzzy coloring around the outlines of a door opening.

Such coloring issues cannot be corrected after post-processing.

Conclusion

Moving objects within a scanned scene has multiple influences on the data quality and can even break a scan in difficult environments. Therefore, best practices during project planning and scanning should be applied at all times to avoid later problems:

• Opening doors in advance and using door stoppers to permanently keep them open,

• Cleaning the environment from movable objects within the planned trajectory (e.g., chairs), and

• Avoiding busy times, keeping the number of pedestrians and cars within the scene as low as possible.

Note: Pause a scan if an unwanted object approaches or when a door needs to be opened.

A scan is then still vulnerable in SLAM performance, but point cloud noise and coloring issues will be avoided since during pauses of the device the captured data will not be used for point cloud. construction in post-processing.