LiDAR Robot Navigation
LiDAR robots navigate using a combination of localization, mapping, as well as path planning. This article will present these concepts and explain how they interact using a simple example of the robot achieving its goal in the middle of a row of crops.
LiDAR sensors are low-power devices that can prolong the battery life of robots and reduce the amount of raw data required for localization algorithms. This allows for more variations of the SLAM algorithm without overheating the GPU.
LiDAR Sensors
The core of lidar systems is its sensor that emits laser light pulses into the environment. These light pulses bounce off the surrounding objects in different angles, based on their composition. The sensor records the time it takes for each return, which is then used to calculate distances. The sensor is typically placed on a rotating platform which allows it to scan the entire area at high speeds (up to 10000 samples per second).
LiDAR sensors can be classified according to whether they're intended for use in the air or on the ground. Airborne lidars are typically connected to helicopters or an UAVs, which are unmanned. (UAV). Terrestrial LiDAR systems are generally mounted on a stationary robot platform.
To accurately measure distances, the sensor needs to be aware of the precise location of the robot at all times. This information is typically captured through an array of inertial measurement units (IMUs), GPS, and time-keeping electronics. LiDAR systems make use of sensors to compute the precise location of the sensor in space and time. This information is then used to create an 3D map of the environment.
lidar vacuum mop scanners can also identify different kinds of surfaces, which is especially beneficial when mapping environments with dense vegetation. When a pulse passes a forest canopy, it is likely to produce multiple returns. The first return is usually attributable to the tops of the trees while the last is attributed with the surface of the ground. If the sensor records these pulses separately, it is called discrete-return LiDAR.
The Discrete Return scans can be used to study surface structure. For
lidar Robot navigation example forests can result in an array of 1st and 2nd return pulses, with the final big pulse representing bare ground. The ability to separate these returns and store them as a point cloud allows for the creation of detailed terrain models.
Once an 3D map of the surrounding area is created and the robot has begun to navigate based on this data. This involves localization, constructing a path to get to a destination and dynamic obstacle detection. This is the process of identifying new obstacles that aren't visible in the original map, and then updating the plan accordingly.
SLAM Algorithms
SLAM (simultaneous mapping and localization) is an algorithm which allows your robot to map its surroundings and then identify its location relative to that map. Engineers utilize this information for a variety of tasks, including planning routes and obstacle detection.
To use SLAM your
robot vacuum with lidar and camera has to have a sensor that gives range data (e.g. A computer that has the right software to process the data and either a camera or laser are required. You also need an inertial measurement unit (IMU) to provide basic information on your location. The result is a system that will precisely track the position of your robot in an unspecified environment.
The SLAM process is complex, and many different back-end solutions are available. Whatever solution you choose to implement an effective SLAM it requires a constant interaction between the range measurement device and the software that extracts the data and also the vehicle or robot. This is a highly dynamic procedure that can have an almost infinite amount of variability.
When the robot moves, it adds scans to its map. The SLAM algorithm compares these scans with previous ones by making use of a process known as scan matching. This allows loop closures to be established. The SLAM algorithm adjusts its robot's estimated trajectory when loop closures are identified.
The fact that the environment can change over time is another factor that complicates SLAM. For example, if your robot is walking through an empty aisle at one point, and then comes across pallets at the next location it will be unable to connecting these two points in its map. This is where the handling of dynamics becomes important, and this is a standard characteristic of modern Lidar SLAM algorithms.
Despite these difficulties however, a properly designed SLAM system is extremely efficient for navigation and 3D scanning. It is particularly useful in environments that don't rely on GNSS for its positioning, such as an indoor factory floor. It is important to remember that even a well-configured SLAM system may have errors. It is essential to be able recognize these flaws and understand how they impact the SLAM process in order to rectify them.
Mapping
The mapping function creates a map for a robot's surroundings. This includes the robot, its wheels, actuators and everything else that falls within its field of vision. This map is used for localization, path planning, and obstacle detection. This is an area in which 3D lidars are particularly helpful since they can be effectively treated as the equivalent of a 3D camera (with one scan plane).
Map building can be a lengthy process however, it is worth it in the end. The ability to create a complete, coherent map of the robot's surroundings allows it to perform high-precision navigation, as as navigate around obstacles.
As a general rule of thumb, the higher resolution the sensor, more precise the map will be. Not all robots require maps with high resolution. For instance floor sweepers may not require the same level of detail as a robotic system for industrial use operating in large factories.
There are a variety of mapping algorithms that can be utilized with LiDAR sensors. Cartographer is a very popular algorithm that uses the two-phase pose graph optimization technique. It corrects for drift while maintaining an accurate global map. It is especially useful when used in conjunction with Odometry.
GraphSLAM is another option, that uses a set linear equations to represent the constraints in a diagram. The constraints are represented by an O matrix, and a vector X. Each vertice in the O matrix contains a distance from an X-vector landmark. A GraphSLAM update consists of the addition and subtraction operations on these matrix elements, with the end result being that all of the O and X vectors are updated to accommodate new information about the robot.
SLAM+ is another useful mapping algorithm that combines odometry with mapping using an Extended Kalman filter (EKF). The EKF alters the uncertainty of the robot's location as well as the uncertainty of the features that were drawn by the sensor. This information can be utilized by the mapping function to improve its own estimation of its position and update the map.
Obstacle Detection
A robot should be able to perceive its environment to overcome obstacles and reach its goal. It makes use of sensors like digital cameras, infrared scans laser radar, and sonar to determine the surrounding. Additionally, it employs inertial sensors to determine its speed, position and orientation. These sensors enable it to navigate safely and avoid collisions.
One important part of this process is obstacle detection that consists of the use of an IR range sensor to measure the distance between the robot and the obstacles. The sensor can be attached to the vehicle, the robot or even a pole. It is crucial to keep in mind that the sensor can be affected by a myriad of factors, including wind, rain and fog. Therefore, it is essential to calibrate the sensor prior every use.