#002- Is it possible to deliver a point cloud directly from the field?
Wouldn’t it be great to send your scanning data to your customer directly from the site?
Let’s continue the Trimble X7 review. As you could read in the first part of the article I had a chance to test out TLS (terrestrial laser scanner) from Trimble. The data suppose to be ready straight from the fieldwork. The surveyor can upload the point cloud directly to some cloud like Google Drive, Dropbox, or any other, and send the link to the customer. In this part of the article, I will focus on the point cloud and picture quality and will check does the connections among scan stations created directly in the Trimble Perspective are good enough to be a final deliverance?
Trimble Perspective deliverance
As I mentioned in Part 1 of this article, Trimble Perspective is the software installed on a Windows tablet and it lets to control acquisition from the Trimble X7 scanner. Users can control the scanner, adjust the scanning parameters, constrain single scans, and export data ready for delivery.
It’s a complex and advanced field software that gives a lot of possibilities for the end-user. Constrained and refined point cloud together with the quality report can be exported to well-known file formats like e57, LAS, PTX, and RCP. Supported formats are also Trimble’s TDX and TZF or Pointools POD. Those formats give the user deliverance flexibility straight from the fieldwork.
The question that comes to mind, however, whether the quality of the data is sufficient to send them to the client? To investigate it I used two samples of data. Set number one consists of 19 scans collected with parameters: 4 minutes scan time, pictures, and auto white balance.
Four scans were collected in the open area — trees, ground, stream, small walk-bridge, and facades of buildings in about 10m to 30m from the scanner positions. The rest of the scans were collected from nearby buildings which were simpler for the algorithm to combine the data together.
Open area scans haven’t been constrained in the field (Trimble Perspective) because of the constraining failures (even though 19% of overlap between scans) so after 10 minutes of trying, I just gave up and decided to complete data processing on a PC. The solution would be to have one more scan station in between but in this particular case, a scan would be done on the bridge which was not stable.
Set number two consists of 11 scan stations with the same parameters as the previous dataset. In this example, I had also trouble combining data. I occur that between scans on opposite sides of the two-line street where distance was approx. 22m, scans couldn’t be connected by the auto-alignment feature. I had to connect them manually but the result was not impressive. An extra scan would be a solution to fix it and give more overlap among scans but then I would have to do it in the middle of the cross-road. I believe the Trimble Perspective would perfectly work with the data come from a narrow area where are a lot of different surfaces, or indoor projects.
Data constrained in Leica Cyclone Core 2021
Both datasets were imported to Leica Cyclone Core software as e57 and PTX formats. I didn’t have any troubles regarding import. Data appear as regular raw colorized point clouds placed in correct spots with each other (constrained in Trimble Perspective). The problematic spot in set no.1 was easily fixed which shows that the overlap was good enough to constrain data. Data from dataset no.2 have been also fixed/constrained in Cyclone.
- The first constrain contained the just imported e57/PTX files. They suppose to be placed in the right places as good as Trimble Perspective lets to do that. It is always the reference data.
- The second constrain contained reproduced single connections (cloud2cloud) as it was done in Trimble Perspective
- The third one contained a dense network of connections among scan stations which suppose to be the proper final constrained data ready to deliver. Connections were created in Leica Cyclone Core with an auto-add cloud constrains feature which automatically finds overlaps among scans. The data was manually examined in cross-sections.
Comparison methodology
The first comparison was done between the first and second constrain. It let me know if and what is the difference between those constraints in software from the two competitors on the market. In theory, they should be similar or very close to similar.
The second comparison was done between the second and third constrain. This one indicated the quality of constrained, refined, and ready-for-delivery data consists of single connections between scan stations, and the dense network of connections among scans created in Cyclone Core.
Both comparisons were made in Leica Cyclone 3DR 2021 and visualized by colorized distance intervals.
Dataset no.1
Dataset no.1 was collected in a city area. It begins in the open area where are trees and a stream and continued to the more narrow area between 1–2 storage buildings. Below you can see tables show the C2C (cloud to cloud) constrains quality.
- Number of single connections — 18
- Number of connections in dense constraining network — 59
The first comparison shows the small differences, especially in the height. The difference oscillates between 0cm and 3cm. The most significant difference is visible in one area where the cobblestone ground differs. The horizontal plane seems to be almost the same except a single spot with shiny, metal wall plates mounted on quite a big wall’s surface. This type of wall can be tricky for the cloud2cloud constraining type. Here we can assume that the Trimble Perspective did a pretty good job.
The second comparison presents bigger differences. I again occur height fluctuations which give higher numbers and appear in the same area. The differences oscillate between 0cm and 4.5cm which is more than in the previous comparison and worry a bit. It was only a 19 scans dataset so I wonder what could happen in the case of 150 or more scans project. Do the user can still rely on the algorithm implemented in Trimble Perspective? Can the customer believe that the data is proper and ready to use or should ask for data processed in an office?
Dataset no.2
Dataset no.2 was collected under the road bridge and continued to the top of the same bridge. It is looped data — the first and the last scan overlapped.
- Number of single connections — 10
- Number of connections in dense constraining network — 42
Unfortunately, the first comparison wasn’t able to be done. The data from the field wasn’t properly combined because of constraining trouble in Trimble Perspective.
Luckily, the second comparison shows the difference between data based on single connections and dense network of connections among scan stations.
Summary
As you could read in this article, point clouds that come from Trimble are impressive but not perfect. Can the data collected with Trimble X7 be delivered to a customer directly from the field? The answer is not obvious and depends on several factors. These factors are:
- Amount of scan stations in a project — more scans give lower quality of entire scans network
- Overlap quality between scans — higher overlap percentage lower possibility of rotations between scans
- Loop scanning method or linear scanning method — loop method improve the quality of final data
- Required accuracy in a project
In my opinion, the quality of the data is very good and comparable to its competitors in the class like Leica RTC360 or Faro S series. It has some disadvantages like pictures white-balance in direct sunlight or improperly leveled single scan (check below) but I believe it can be fixed by the firmware update. There are no problems with data import failures regarding e57, LAS, and PTX formats.
I think every user of X7 should consider post-processing of the data before delivering it but there are for sure some scenarios when a point cloud could be delivered straight from the construction site.
Below some pictures which show the quality of the point clouds and photos. Read the captions :)
