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LiDAR Robot Navigation LiDAR robot navigation is a sophisticated combination of localization, mapping and path planning. This article will introduce the concepts and demonstrate how they function using an example in which the robot is able to reach the desired goal within a row of plants. LiDAR sensors are low-power devices that can extend the battery life of robots and decrease the amount of raw data needed to run localization algorithms. lidar robot vacuum cleaner allows for a greater number of iterations of the SLAM algorithm without overheating the GPU. LiDAR Sensors The sensor is the core of the Lidar system. It releases laser pulses into the environment. These pulses bounce off objects around them in different angles, based on their composition. The sensor measures how long it takes each pulse to return and uses that data to calculate distances. Sensors are mounted on rotating platforms that allow them to scan the surrounding area quickly and at high speeds (10000 samples per second). LiDAR sensors are classified according to whether they are designed for airborne or terrestrial application. Airborne lidars are typically mounted on helicopters or an UAVs, which are unmanned. (UAV). Terrestrial LiDAR systems are usually placed on a stationary robot platform. To accurately measure distances, the sensor must be aware of the exact location of the robot at all times. This information is usually gathered by a combination of inertial measurement units (IMUs), GPS, and time-keeping electronics. LiDAR systems use sensors to compute the precise location of the sensor in space and time, which is then used to build up an image of 3D of the surroundings. LiDAR scanners can also detect different types of surfaces, which is particularly useful when mapping environments that have dense vegetation. For instance, when the pulse travels through a forest canopy, it is likely to register multiple returns. The first return is usually associated with the tops of the trees while the last is attributed with the ground's surface. If the sensor records these pulses separately, it is called discrete-return LiDAR. Distinte return scans can be used to study surface structure. For instance the forest may produce an array of 1st and 2nd return pulses, with the final big pulse representing bare ground. The ability to separate and record these returns as a point-cloud allows for precise models of terrain. Once a 3D map of the surroundings is created and the robot has begun to navigate based on this data. This involves localization, constructing an appropriate path to get to a destination and dynamic obstacle detection. This is the process that detects new obstacles that were not present in the original map and updates the path plan in line with the new obstacles. SLAM Algorithms SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to build an outline of its surroundings and then determine the location of its position in relation to the map. Engineers utilize this information for a variety of tasks, such as the planning of routes and obstacle detection. To be able to use SLAM, your robot needs to be equipped with a sensor that can provide range data (e.g. a camera or laser) and a computer with the appropriate software to process the data. Also, you need an inertial measurement unit (IMU) to provide basic information about your position. The result is a system that will accurately track the location of your robot in an unspecified environment. The SLAM system is complicated and offers a myriad of back-end options. Whatever solution you choose the most effective SLAM system requires constant interaction between the range measurement device and the software that extracts the data and the robot or vehicle itself. This is a highly dynamic process that is prone to an infinite amount of variability. When the robot moves, it adds scans to its map. The SLAM algorithm compares these scans to the previous ones using a process called scan matching. This allows loop closures to be established. The SLAM algorithm adjusts its estimated robot trajectory when a loop closure has been detected. The fact that the surrounding can change in time is another issue that can make it difficult to use SLAM. If, for example, your robot is navigating an aisle that is empty at one point, and it comes across a stack of pallets at another point it may have trouble connecting the two points on its map. This is when handling dynamics becomes crucial and is a standard feature of the modern Lidar SLAM algorithms. SLAM systems are extremely efficient in 3D scanning and navigation despite the challenges. It is particularly useful in environments that don't permit the robot to depend on GNSS for positioning, such as an indoor factory floor. However, it's important to remember that even a well-configured SLAM system may have mistakes. It is crucial to be able to detect these flaws and understand how they impact the SLAM process to fix them. Mapping The mapping function builds an outline of the robot's surrounding which includes the robot as well as its wheels and actuators as well as everything else within its view. The map is used to perform localization, path planning, and obstacle detection. This is an area in which 3D Lidars can be extremely useful as they can be treated as an 3D Camera (with one scanning plane). The process of creating maps takes a bit of time, but the results pay off. The ability to build a complete, coherent map of the robot's environment allows it to carry out high-precision navigation, as well as navigate around obstacles. As a rule of thumb, the higher resolution of the sensor, the more accurate the map will be. Not all robots require high-resolution maps. For instance a floor-sweeping robot might 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. One of the most well-known algorithms is Cartographer which utilizes the two-phase pose graph optimization technique to correct for drift and maintain a uniform global map. It is especially efficient when combined with Odometry data. GraphSLAM is a different option, that uses a set linear equations to represent constraints in diagrams. The constraints are represented as an O matrix and an X vector, with each vertex of the O matrix containing the distance to a point on the X vector. A GraphSLAM update consists of a series of additions 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 and mapping using an Extended Kalman filter (EKF). The EKF changes the uncertainty of the robot's location as well as the uncertainty of the features that were recorded by the sensor. The mapping function can then utilize this information to improve its own position, allowing it to update the base map. Obstacle Detection A robot needs to be able to sense its surroundings in order to avoid obstacles and reach its final point. It uses sensors like digital cameras, infrared scanners sonar and laser radar to sense its surroundings. It also utilizes an inertial sensors to determine its speed, position and the direction. These sensors aid in navigation in a safe manner and prevent collisions. A range sensor is used to determine the distance between the robot and the obstacle. The sensor can be placed on the robot, inside a vehicle or on poles. It is crucial to keep in mind that the sensor could be affected by a variety of elements like rain, wind and fog. Therefore, it is essential to calibrate the sensor prior to every use. The results of the eight neighbor cell clustering algorithm can be used to detect static obstacles. This method isn't particularly precise due to the occlusion caused by the distance between laser lines and the camera's angular speed. To overcome this problem multi-frame fusion was implemented to improve the effectiveness of static obstacle detection. The method of combining roadside unit-based and obstacle detection using a vehicle camera has been proven to increase the data processing efficiency and reserve redundancy for future navigational operations, like path planning. The result of this method is a high-quality picture of the surrounding area that is more reliable than a single frame. The method has been tested with other obstacle detection methods, such as YOLOv5, VIDAR, and monocular ranging in outdoor tests of comparison. The results of the test revealed that the algorithm was able accurately identify the location and height of an obstacle, in addition to its rotation and tilt. It also showed a high ability to determine the size of obstacles and its color. The method also demonstrated solid stability and reliability, even when faced with moving obstacles.