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LiDAR Robot Navigation LiDAR robots move using a combination of localization and mapping, as well as path planning. This article will introduce the concepts and show how they work by using a simple example where the robot is able to reach a goal within the space of a row of plants. LiDAR sensors have low power requirements, allowing them to increase a robot's battery life and decrease the amount of raw data required for localization algorithms. This allows for a greater number of iterations of SLAM without overheating GPU. LiDAR Sensors The heart of a lidar system is its sensor that emits laser light in the environment. These light pulses strike objects and bounce back to the sensor at various angles, depending on the structure of the object. The sensor monitors the time it takes for each pulse to return and then uses that information to determine distances. The sensor is typically mounted on a rotating platform allowing it to quickly scan the entire area at high speeds (up to 10000 samples per second). LiDAR sensors can be classified according to whether they're designed for airborne application or terrestrial application. Airborne lidar systems are typically connected to aircrafts, helicopters or UAVs. (UAVs). Terrestrial LiDAR systems are typically mounted on a stationary robot platform. To accurately measure distances, the sensor must be aware of the precise location of the robot at all times. This information is gathered using a combination of inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems use sensors to compute the precise location of the sensor in space and time, which is then used to create an image of 3D of the surrounding area. LiDAR scanners can also identify different kinds of surfaces, 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 is associated with the ground's surface. If the sensor records these pulses separately and is referred to as discrete-return LiDAR. Discrete return scans can be used to study surface structure. For instance, a forested region could produce a sequence of 1st, 2nd, and 3rd returns, with a final large pulse that represents the ground. The ability to divide these returns and save them as a point cloud allows to create detailed terrain models. Once a 3D model of the environment is built the robot will be able to use this data to navigate. This process involves localization and making a path that will reach a navigation “goal.” It also involves dynamic obstacle detection. This is the process of identifying obstacles that aren't visible on the original map and updating the path plan accordingly. SLAM Algorithms SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to build a map of its environment and then determine where it is relative to the map. Engineers utilize this information for a variety of tasks, including planning routes and obstacle detection. To use SLAM the robot needs to have a sensor that provides range data (e.g. A computer with the appropriate software for processing the data as well as a camera or a laser are required. You also need an inertial measurement unit (IMU) to provide basic positional information. The system can determine your robot's exact location in a hazy environment. The SLAM process is a complex one, and many different back-end solutions exist. No matter which solution you select for an effective SLAM is that it requires constant interaction between the range measurement device and the software that collects data, as well as the vehicle or robot. It is a dynamic process that is almost indestructible. As the robot moves, it adds scans to its map. The SLAM algorithm then compares these scans with the previous ones using a method called scan matching. This allows loop closures to be identified. The SLAM algorithm is updated with its robot's estimated trajectory when a loop closure has been identified. The fact that the environment can change over time is another factor that complicates SLAM. For instance, if your robot is navigating an aisle that is empty at one point, and then encounters a stack of pallets at another point, it may have difficulty matching the two points on its map. Handling dynamics are important in this scenario, and they are a feature of many modern Lidar SLAM algorithm. Despite these challenges however, a properly designed SLAM system is extremely efficient for navigation and 3D scanning. It is especially beneficial in environments that don't let the robot rely on GNSS positioning, like an indoor factory floor. However, it is important to note that even a properly configured SLAM system can be prone to errors. It is essential to be able to detect these issues and comprehend how they impact the SLAM process in order to fix them. Mapping The mapping function creates a map of the robot's surrounding which includes the robot as well as its wheels and actuators, and everything else in the area of view. This map is used to aid in localization, route planning and obstacle detection. This is an area where 3D lidars are particularly helpful since they can be used as a 3D camera (with one scan plane). Map creation can be a lengthy process, but it pays off in the end. The ability to build a complete, consistent map of the robot's surroundings allows it to perform high-precision navigation as well as navigate around obstacles. As a rule, the greater the resolution of the sensor then the more accurate will be the map. Not all robots require high-resolution maps. For example a floor-sweeping robot might not require the same level detail as a robotic system for industrial use navigating large factories. This is why there are many different mapping algorithms for use with LiDAR sensors. Cartographer is a very popular algorithm that utilizes a two-phase pose graph optimization technique. It corrects for drift while maintaining a consistent global map. It is particularly efficient when combined with the odometry information. GraphSLAM is a different option, which utilizes a set of linear equations to represent the constraints in the form of a diagram. The constraints are represented as an O matrix, as well as an vector X. Each vertice in the O matrix is an approximate distance from a landmark on X-vector. A GraphSLAM Update is a sequence of subtractions and additions to these matrix elements. The end result is that both the O and X Vectors are updated to account for the new observations made by the robot. Robot Vacuum Mops is SLAM+, which combines mapping and odometry using an Extended Kalman filter (EKF). The EKF updates the uncertainty of the robot's location as well as the uncertainty of the features that were mapped by the sensor. The mapping function is able to utilize this information to better estimate its own position, allowing it to update the base map. Obstacle Detection A robot needs to be able to see its surroundings so it can avoid obstacles and reach its final point. It uses sensors like digital cameras, infrared scanners laser radar and sonar to determine its surroundings. It also uses inertial sensor to measure its speed, location and orientation. These sensors assist it in navigating in a safe way and avoid collisions. One important part of this process is the detection of obstacles that consists of the use of sensors to measure the distance between the robot and obstacles. The sensor can be mounted on the robot, inside an automobile or on the pole. It is crucial to remember that the sensor is affected by a myriad of factors such as wind, rain and fog. It is important to calibrate the sensors prior to each use. A crucial step in obstacle detection is the identification of static obstacles, which can be done by using the results of the eight-neighbor cell clustering algorithm. This method isn't very accurate because of the occlusion caused by the distance between laser lines and the camera's angular speed. To address this issue multi-frame fusion was employed to improve the effectiveness of static obstacle detection. The method of combining roadside unit-based as well as obstacle detection using a vehicle camera has been shown to improve the efficiency of processing data and reserve redundancy for subsequent navigation operations, such as path planning. The result of this method is a high-quality image of the surrounding environment that is more reliable than a single frame. The method has been tested against other obstacle detection methods like YOLOv5 VIDAR, YOLOv5, as well as monocular ranging in outdoor comparative tests. The results of the experiment revealed that the algorithm was able to correctly identify the location and height of an obstacle, as well as its rotation and tilt. It also showed a high performance in detecting the size of an obstacle and its color. The algorithm was also durable and steady even when obstacles moved.