LiDAR SLAM
What are its advantages?

 

SLAM-based technology is being used more and more frequently, and has been validated in different fields of engineering, such as mining and construction. The technology is in a full growth stage, but what are its advantages?, in this context we can mention some:

  • ⨭ Shorter execution times for field work.
  • ⨭ Reduced operator exposure to difficult or unsafe environments.
  • ⨭ Use in hard-to-reach environments.
  • ⨭ Flexibility.
  • ⨭ Generation of a point cloud with high representativeness of the environment.

 

Overview of the SLAM LiDAR process

The incorporation of the inertial sensor (IMU) has been very helpful in collecting position data within the measurement environment.

 

Estimates of the position of characteristic points improve when these characteristics are observed more often. The iterative process of the SLAM algorithm helps to obtain greater precision, which means that the more iterations, the greater the precision in the pose.

 

What are the challenges of SLAM?

We have always talked about the advantages of SLAM in topography, but what are the challenges that we, as professionals, have to overcome?

SLAM, like all technology, is not exempt from errors; localization errors accumulate over time and produce a margin of error, also known as drift. Drifts can occur more frequently in linear trajectories; in many cases, these errors can be avoided with some care in data collection.

 

Figure 1: Corresponds to a point cloud of a tunnel with drift, the white line corresponds to a total station sight.

Figure 2: Corresponds to a re-processing with the SLAM algorithm, which rectifies the drift. It should be noted that not all manufacturers have this re-processing feature. GeoSLAM with its Connect software provides us with this option.

Figure 3: Comparison between both clouds.

 

Some recommendations for an expected product

To avoid these deviations in areas with loop closures, it is advisable to start the measurement and finish in the same place, so that SLAM can estimate a closure error.

For linear trajectories, a good practice is to divide the measurement, for example, if you have a 1km tunnel, we should separate the measurements by sections, with the corresponding overlaps.

If there are places with few particular elements, very homogeneous places, it is advisable to incorporate elements that are recognizable by the SLAM algorithm, with this we could avoid possible errors.

In the case of cloud referencing, depending on the SLAM algorithm being used, it will help to improve drift, in the case of GeoSLAM with its Connect software, it provides a very interesting tool in this aspect, but in most cases, only the point cloud can be referenced.

The mobility, versatility, and precision that SLAM provides us are benefits whose scope we need to understand to achieve the expected products.

We await you at our LiDAR Month event, where we will discuss the benefits and best practices of SLAM technology.