LiDAR Robot Navigation

LiDAR robot navigation is a complicated combination of localization, mapping and path planning. This article will introduce these concepts and show how they function together with an example of a robot achieving a goal within the middle of a row of crops.

LiDAR sensors have modest power requirements, allowing them to prolong the battery life of a robot and reduce the raw data requirement for localization algorithms. This allows for more versions of the SLAM algorithm without overheating the GPU.

LiDAR Sensors

The sensor is the core of the Lidar system. It emits laser pulses into the environment. The light waves bounce off the surrounding objects in different angles, based on their composition. The sensor monitors the time it takes for each pulse to return, and utilizes that information to determine distances. The sensor is typically placed on a rotating platform, allowing it to quickly scan the entire surrounding area at high speed (up to 10000 samples per second).

LiDAR sensors are classified based on their intended applications on land or in the air. Airborne lidar systems are typically mounted on aircrafts, helicopters or unmanned aerial vehicles (UAVs). Terrestrial LiDAR is usually installed on a robot platform that is stationary.

To accurately measure distances the sensor must always know the exact location of the robot. This information is typically captured by an array of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are employed by LiDAR systems to calculate the precise location of the sensor within space and time. This information is then used to create a 3D model of the surrounding environment.

LiDAR scanners can also be used to detect different types of surface which is especially useful when mapping environments that have dense vegetation. When a pulse passes through a forest canopy, it is likely to generate multiple returns. The first one is typically attributable to the tops of the trees, while the second one is attributed to the surface of the ground. If the sensor records these pulses separately and is referred to as discrete-return LiDAR.

The Discrete Return scans can be used to determine surface structure. For instance, a forested area could yield the sequence of 1st 2nd and 3rd return, with a final large pulse representing the ground. The ability to separate these returns and record them as a point cloud makes it possible for the creation of detailed terrain models.

Once a 3D model of the environment is constructed the robot will be able to use this data to navigate. This involves localization and creating a path to get to a navigation "goal." It also involves dynamic obstacle detection. The latter is the process of identifying new obstacles that aren't visible in the original map, and Lidar robot navigation adjusting the path plan in line with the new obstacles.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its surroundings and then determine its position relative to that map. Engineers utilize the information for a number of tasks, such as planning a path and identifying obstacles.

To allow SLAM to work, your robot must have an instrument (e.g. A computer with the appropriate software for processing the data and a camera or a laser are required. Also, you will require an IMU to provide basic positioning information. The system can track your robot vacuum cleaner lidar's exact location in an unknown environment.