LiDAR data is represented in the form of a point cloud in which each of the points contains a multitude of information. The point cloud thus forms an exact 3D image of the environment. Afterwards, algorithms are used to evaluate and obtain meaningful information from this point cloud data for specific applications. One algorithm that enables a wide variety of applications is the definition of a particular zone in the sensor’s field of view. This article will discover how this software feature works and what applications can be implemented with it.
Protect valuable items from accidental damage
A valuable painting is displayed by a museum hanging on the wall. To ensure that it is not damaged by passers-by or careless backpack carriers, a secured area has been set up around the exhibit. How can LiDAR data help ensure that no one really enters the roped-off area?
This can be achieved by mounting the LiDAR sensor in the room so that it is surveilling the critical area. The first step is defining the scene’s background by creating a 3D image of the room without any people. This differentiates between the static objects in the room and the dynamic ones, which can potentially trigger an alarm.
Using this background, a zone is defined that corresponds to the restricted area around the painting, as shown in the figure. In the next step, the detection threshold of the number of points in the secured perimeter are defined. For example, if someone is just leaning over the barrier to take a picture, a small number of points are detected in the predefined space. If this number is below the threshold, the person is not yet identified as having entered the restricted area. However, if the photographing visitor now takes a step beyond the barrier, he or she is recognized as an object in the sensitive zone. In this case, a museum employee in the vicinity can be informed or an alarm can be triggered for the security personnel.
Numerous application possibilities for zones
This technique can be employed in various applications. In train stations, for example, the rail tracks can be defined as secured areas, and a train could be alerted and stopped if any significant activity is detected on the track. Company premises, where valuable goods are stored, can be protected in this way as well. If a particular area is defined as a secured perimeter, an alarm can be triggered as soon as someone enters. The definable threshold value offers great importance here against false alarms. If, for instance, a cat runs through the zone or some leaves fall from a tree, they will not trigger any alerts as that doesn’t cross the threshold value.
Valuable information with protected privacy
LiDAR can also provide valuable information in and around the city. If, for example, it is necessary to determine the number of pedestrians in a particular pedestrian zone, the areas in front of the shop windows can be surveyed using a LiDAR. Consequently, a lot of information can be extracted from the point cloud in real-time, such as the number of people entering and leaving the defined zone and the duration of their stay. This information can vital for businesses and city administration and can be collected using the same algorithm as in the museum example. In addition, LiDAR has the decisive advantage over cameras in maintaining the privacy of the detected people, as they are only recognized as point cloud silhouettes. Because LiDAR only records distances in 3D between the sensor and the objects, in this case a person, and cannot collect any other details like hair or facial features, the person is detected merely as an intrusion in the defined area.
Avoid annoying search for parking spots
Just as zoning can be used to determine whether a person is entering an area that should not be entered, it is also easy to determine whether certain areas are occupied or not. An application example could be parking spot detection, where LiDAR sensors are integrated into streetlights along a roadside parking strip. In terms of the point cloud, the individual parking spaces are each defined as zones. If a 3D object, in this case a vehicle, is detected, the parking spot is marked as occupied. If this information is forwarded to navigation systems, car drivers could be immediately navigated to the next free parking spot near their destination address. The algorithm’s ability to identify the object’s size and its occupancy period in the defined area ensures accuracy in detection, meaning no car would be diverted just because a pedestrian took a shortcut across the parking lot.
The same application can be applied in seating areas. Particularly at times when people should keep their distance and not sit directly next to each other, LiDAR sensors can identify, for example, in large cafeterias, free seats with sufficient distance to the seating neighbors. The algorithm can also be used to count occupied seats and thus check whether the maximum occupancy limit are being exceeded.
Versatile applications for a smart future
These are just a few examples of applications that can be implemented with the help of environment detection by LiDAR and defining a zone in a point cloud. This modern measurement technology is thus ushering a wide variety of industries and sectors into a smarter future.