The growing market of Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) has led to advanced hard- and software development in recent years. SLAM (simultaneous mapping and localization) is an essential algorithm for autonomous vehicles and robots and is frequently adopted in this segment. It is also one of the main reasons for the rapid growth of the sector in recent years.
As the name suggests, SLAM is an algorithm that is used for location and mapping purposes at the same time. Lidar plays a key role in a system that utilizes SLAM, as it serves as the necessary hardware for this software algorithm. SLAM uses the point cloud of the lidar to create a map of the environment that gets more accurate during the mapping process. With the help of the generated 3D map, the algorithm then calculates the position based on the current sensor data. Sometimes, external devices like GPS are used in conjunction with SLAM for assistance.
Advantages of using SLAM
In comparison to using a pre-defined map, SLAM uses real-time information and is able to dynamically adapt to a change in the environment. For instance, imagine an autonomous vacuum cleaner that proceeds on a static map that was defined prior to the cleaning process. Although the robot may be able to navigate seamlessly in some cases, it will struggle when it encounters new objects - like chairs that were moved to a new position. With SLAM, the map is continuously updated and therefore allows the robots to avoid obstacles, pedestrians, or even other robots. However, the map does not have to be re-generated every time the robot starts a mission, as a current version of the map is saved at the end of a mission.
Lidar- vs. Camera-based SLAM
In comparison to cameras, lidar-based SLAM is able to accurately identify depth information, like the exact distances to objects in the environment. In the case of using a camera, the video has to undergo extensive post-processing in order to get an estimate of the distances. Therefore, lidar is the overall more reliable and accurate solution.