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LiDAR Robot Navigation
LiDAR robots navigate using a combination of localization, mapping, and also path planning. This article will present these concepts and demonstrate 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 life of batteries on robots and decrease the amount of raw data required to run localization algorithms. This allows for more iterations of SLAM without overheating GPU.
LiDAR Sensors
The sensor is the heart of the Lidar system. It emits laser pulses into the surrounding. These pulses hit surrounding objects and bounce back to the sensor at a variety of angles, based on the structure of the object. The sensor monitors the time it takes each pulse to return and uses 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 can be classified based on the type of sensor they're designed for, whether airborne application or terrestrial application. Airborne lidars are usually attached to helicopters or unmanned aerial vehicles (UAV). Terrestrial LiDAR systems are usually placed on a stationary robot platform.
To accurately measure distances, the sensor must always know the exact location of the robot. This information is typically captured through an array of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are utilized by LiDAR systems to calculate the exact position of the sensor within the space and time. The information gathered is used to create a 3D model of the surrounding environment.
LiDAR scanners are also able to identify different surface types, which is particularly beneficial for mapping environments with dense vegetation. When a pulse crosses a forest canopy, it will typically produce multiple returns. Typically, the first return is attributable to the top of the trees while the final return is attributed to the ground surface. If the sensor records these pulses separately this is known as discrete-return LiDAR.
The use of Discrete Return scanning can be helpful in analysing the structure of surfaces. For instance, a forest area could yield an array of 1st, 2nd and 3rd returns with a final, large pulse that represents the ground. The ability to separate and store these returns in a point-cloud permits detailed terrain models.
Once a 3D model of environment is built, the robot will be equipped to navigate. This process involves localization, constructing a path to get to a destination,' and dynamic obstacle detection. This process detects new obstacles that are not listed in the original map and adjusts the path plan in line with the new obstacles.
SLAM Algorithms
SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to create an image of its surroundings and then determine the position of the robot in relation to the map. Engineers utilize this information for a range of tasks, such as the planning of routes and obstacle detection.
To utilize SLAM your robot has to have a sensor that provides range data (e.g. A computer with the appropriate software for processing the data as well as cameras or lasers are required. You will also require an inertial measurement unit (IMU) to provide basic information on your location. The system will be able to track your robot's exact location in a hazy environment.
The SLAM system is complicated and there are a variety of back-end options. Whatever solution you choose for an effective SLAM it requires constant communication between the range measurement device and the software that extracts the data and also the vehicle or robot. This is a dynamic procedure with a virtually unlimited 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 created. When a loop closure is discovered when loop closure is detected, the SLAM algorithm utilizes this information to update its estimated robot trajectory.
The fact that the surrounding changes over time is another factor that makes it more difficult for SLAM. For instance, if your robot is walking along an aisle that is empty at one point, and then encounters a stack of pallets at a different point it might have trouble finding the two points on its map. Handling dynamics are important in this scenario and are a part of a lot of modern Lidar SLAM algorithms.
Despite these difficulties, a properly configured SLAM system can be extremely effective for navigation and 3D scanning. It is particularly beneficial in situations where the robot isn't able to rely on GNSS for positioning for example, an indoor factory floor. It is important to keep in mind that even a properly configured SLAM system can be prone to errors. To fix these issues, it is important to be able to spot them and comprehend their impact on the SLAM process.
Mapping
The mapping function creates an image of the robot's surrounding, which includes the robot as well as its wheels and actuators as well as everything else within the area of view. The map is used for localization, route planning and obstacle detection. This is a field in which 3D Lidars are particularly useful as they can be treated as an 3D Camera (with only one scanning plane).
The map building process may take a while, but the results pay off. The ability to build an accurate and complete map of a robot's environment allows it to move with high precision, and also around obstacles.
As a general rule of thumb, the greater resolution of the sensor, the more accurate the map will be. Not all robots require maps with high resolution. For example, a floor sweeping robot may not require the same level detail as an industrial robotics system that is navigating factories of a large size.
This is why there are a variety of different mapping algorithms that can be used with LiDAR sensors. One popular algorithm is called Cartographer which utilizes two-phase pose graph optimization technique to correct for drift and create an accurate global map. lidar robot vacuum and mop is particularly effective when paired with the odometry.
GraphSLAM is a different option, that uses a set linear equations to represent constraints in the form of a diagram. The constraints are modeled as an O matrix and a the X vector, with every vertice of the O matrix representing a distance to a landmark on the X vector. A GraphSLAM update is the addition and subtraction operations on these matrix elements, and the result is that all of the X and O vectors are updated to accommodate new observations of the robot.
SLAM+ is another useful mapping algorithm that combines odometry with mapping using an Extended Kalman filter (EKF). The EKF changes the uncertainty of the robot's position as well as the uncertainty of the features mapped 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 must be able to sense its surroundings to avoid obstacles and get to its desired point. It uses sensors like digital cameras, infrared scanners, sonar and laser radar to detect its environment. It also makes use of an inertial sensor to measure its speed, position and orientation. These sensors assist it in navigating in a safe way and avoid collisions.
One important part of this process is obstacle detection that involves the use of sensors to measure the distance between the robot and the obstacles. The sensor can be mounted to the robot, a vehicle, or a pole. It is important to remember that the sensor could be affected by many factors, such as wind, rain, and fog. It is important to calibrate the sensors prior each use.
The results of the eight neighbor cell clustering algorithm can be used to identify static obstacles. This method isn't particularly accurate because of the occlusion created by the distance between the laser lines and the camera's angular velocity. To overcome this problem, a method called multi-frame fusion has been employed to increase the accuracy of detection of static obstacles.
The method of combining roadside unit-based and obstacle detection by a vehicle camera has been shown to improve the efficiency of processing data and reserve redundancy for subsequent navigational tasks, like path planning. The result of this method is a high-quality picture of the surrounding environment that is more reliable than one frame. The method has been compared with other obstacle detection methods like YOLOv5 VIDAR, YOLOv5, as well as monocular ranging, in outdoor tests of comparison.
The results of the experiment showed that the algorithm was able accurately identify the location and height of an obstacle, as well as its rotation and tilt. It was also able determine the color and size of an object. The method was also reliable and steady, even when obstacles moved.