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LiDAR Robot Navigation LiDAR robot navigation is a complicated combination of mapping, localization and path planning. This article will explain these concepts and show how they function together with an example of a robot reaching a goal in a row of crops. LiDAR sensors are low-power devices that can prolong the battery life of robots and decrease the amount of raw data required for localization algorithms. This allows for a greater number of iterations of the SLAM algorithm without overheating the GPU. LiDAR Sensors The central component of lidar systems is their sensor that emits pulsed laser light into the environment. These light pulses bounce off the surrounding objects at different angles based on their composition. The sensor is able to measure the amount of time it takes for each return and then uses it to calculate distances. Sensors are positioned on rotating platforms, which allows them to scan the surrounding area quickly and at high speeds (10000 samples per second). LiDAR sensors can be classified according to whether they're designed for applications in the air or on land. Airborne lidars are often attached to helicopters or UAVs, which are unmanned. (UAV). vacuum robot with lidar is usually installed on a stationary robot platform. To accurately measure distances the sensor must be able to determine the exact location of the robot. This information is typically captured by a combination of inertial measuring units (IMUs), GPS, and time-keeping electronics. LiDAR systems make use of sensors to compute the exact location of the sensor in space and time, which is then used to create an 3D map of the surrounding area. LiDAR scanners are also able to recognize different types of surfaces which is especially useful for mapping environments with dense vegetation. When a pulse crosses a forest canopy, it will typically register multiple returns. The first return is attributed to the top of the trees and the last one is related to the ground surface. If the sensor captures these pulses separately this is known as discrete-return LiDAR. Distinte return scanning can be useful in analyzing the structure of surfaces. For instance, a forest region could produce the sequence of 1st 2nd and 3rd return, with a last large pulse that represents the ground. The ability to divide these returns and save them as a point cloud makes it possible to create detailed terrain models. Once a 3D map of the surroundings has been built and the robot has begun to navigate based on this data. This process involves localization and creating a path to reach a navigation “goal.” It also involves dynamic obstacle detection. The latter is the method of identifying new obstacles that aren't present in the original map, and adjusting the path plan accordingly. SLAM Algorithms SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its environment, and then determine its position in relation to the map. Engineers make use of this data for a variety of purposes, including the planning of routes and obstacle detection. For SLAM to function it requires an instrument (e.g. A computer that has the right software to process the data as well as cameras or lasers are required. You will also require an inertial measurement unit (IMU) to provide basic information about your position. The result is a system that can accurately determine the location of your robot in an unknown environment. The SLAM process is extremely complex and a variety of back-end solutions exist. Whatever solution you select for your SLAM system, a successful SLAM system requires a constant interplay between the range measurement device and the software that extracts the data and the vehicle or robot. This is a highly dynamic process that is prone to an infinite amount of variability. As the robot moves about the area, it adds new scans to its map. The SLAM algorithm then compares these scans to the previous ones using a method called scan matching. This allows loop closures to be established. When a loop closure is detected it is then the SLAM algorithm uses this information to update its estimate of the robot's trajectory. The fact that the surroundings changes over time is a further factor that complicates SLAM. For example, if your robot walks through an empty aisle at one point and is then confronted by pallets at the next spot it will be unable to matching these two points in its map. This is when handling dynamics becomes critical and is a common feature of modern Lidar SLAM algorithms. Despite these issues, a properly-designed SLAM system is incredibly effective for navigation and 3D scanning. It is particularly beneficial in situations where the robot can't depend on GNSS to determine its position for example, an indoor factory floor. It is crucial to keep in mind that even a properly configured SLAM system can be prone to errors. It is vital to be able to detect these errors and understand how they impact the SLAM process to correct them. Mapping The mapping function creates a map of the robot's environment that includes the robot, its wheels and actuators and everything else that is in its field of view. This map is used to aid in location, route planning, and obstacle detection. This is an area where 3D lidars can be extremely useful because they can be utilized like the equivalent of a 3D camera (with a single scan plane). Map creation is a long-winded process, but it pays off in the end. The ability to create an accurate, complete map of the robot's environment allows it to conduct high-precision navigation, as well as navigate around obstacles. As a rule, the higher the resolution of the sensor, then the more accurate will be the map. Not all robots require maps with high resolution. For instance floor sweepers might not require the same level detail as an industrial robotic system operating in large factories. This is why there are many different mapping algorithms for use with LiDAR sensors. One popular algorithm is called Cartographer, which uses two-phase pose graph optimization technique to adjust for drift and keep a consistent global map. It is particularly useful when combined with odometry. GraphSLAM is another option, which utilizes a set of linear equations to model the constraints in a diagram. The constraints are represented by an O matrix, and a X-vector. Each vertice of the O matrix is an approximate distance from the X-vector's landmark. A GraphSLAM Update is a sequence of subtractions and additions to these matrix elements. The result is that all O and X Vectors are updated to take into account the latest observations made by the robot. SLAM+ is another useful mapping algorithm that combines odometry with mapping using an Extended Kalman filter (EKF). The EKF updates the uncertainty of the robot's position as well as the uncertainty of the features that were mapped by the sensor. The mapping function can then make use of this information to better estimate its own position, allowing it to update the underlying map. Obstacle Detection A robot needs to be able to see its surroundings in order to avoid obstacles and get to its desired point. It employs sensors such as digital cameras, infrared scans laser radar, and sonar to sense the surroundings. Additionally, it employs inertial sensors to measure its speed and position as well as its orientation. These sensors help it navigate safely and avoid collisions. A range sensor is used to measure the distance between a robot and an obstacle. The sensor can be attached to the robot, a vehicle, or a pole. It is important to keep in mind that the sensor is affected by a myriad of factors like rain, wind and fog. Therefore, it is important to calibrate the sensor before each use. The most important aspect of obstacle detection is the identification of static obstacles, which can be done by using the results of the eight-neighbor cell clustering algorithm. However, this method has a low accuracy in detecting due to the occlusion created by the gap between the laser lines and the speed of the camera's angular velocity, which makes it difficult to identify static obstacles in one frame. To address this issue multi-frame fusion was employed to improve the accuracy of static obstacle detection. The method of combining roadside unit-based and vehicle camera obstacle detection has been proven to increase the efficiency of processing data and reserve redundancy for subsequent navigation operations, such as path planning. The result of this technique is a high-quality image of the surrounding environment that is more reliable than a single frame. The method has been tested with other obstacle detection techniques, such as YOLOv5, VIDAR, and monocular ranging, in outdoor tests of comparison. The results of the study proved that the algorithm was able accurately identify the position and height of an obstacle, as well as its tilt and rotation. It was also able to determine the size and color of an object. The method was also reliable and stable, even when obstacles were moving.