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LiDAR Robot Navigation LiDAR robots move using the combination of localization and mapping, and also path planning. lidar navigation robot vacuum will explain the concepts and explain how they work by using a simple example where the robot reaches the desired goal within a row of plants. LiDAR sensors have modest power requirements, which allows them to increase the life of a robot's battery and reduce the need for raw data for localization algorithms. This allows for a greater number of versions of the SLAM algorithm without overheating the GPU. LiDAR Sensors The core of a lidar system is its sensor that emits pulsed laser light 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 determines how long it takes for each pulse to return and then uses that information to calculate distances. Sensors are mounted on rotating platforms that allow them to scan the area around them quickly and at high speeds (10000 samples per second). LiDAR sensors can be classified based on whether they're designed for airborne application or terrestrial application. Airborne lidars are typically attached to helicopters or unmanned aerial vehicle (UAV). Terrestrial LiDAR systems are usually placed on a stationary robot platform. To accurately measure distances, the sensor needs to know the exact position of the robot at all times. This information is gathered by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are used by LiDAR systems to determine the precise position of the sensor within space and time. This information is then used to create a 3D representation of the surrounding. LiDAR scanners can also be used to recognize different types of surfaces, which is particularly beneficial for mapping environments with dense vegetation. For example, when a pulse passes through a forest canopy, it is likely to register multiple returns. Typically, the first return is attributable to the top of the trees while the last return is attributed to the ground surface. If the sensor records each peak of these pulses as distinct, it is known as discrete return LiDAR. Discrete return scanning can also be useful for analysing the structure of surfaces. For instance, a forested area could yield a sequence of 1st, 2nd and 3rd return, with a last large pulse representing the ground. The ability to separate these returns and store them as a point cloud makes it possible to create detailed terrain models. Once a 3D model of environment is built and the robot is equipped to navigate. This involves localization as well as building a path that will reach a navigation “goal.” It also involves dynamic obstacle detection. This is the method of identifying new obstacles that aren't present in the map originally, and updating the path plan in line with the new obstacles. SLAM Algorithms SLAM (simultaneous mapping and localization) is an algorithm which allows your robot to map its surroundings and then determine its position in relation to the map. Engineers make use of this information for a number of tasks, such as path planning and obstacle identification. To utilize SLAM, your 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 either a camera or laser are required. You will also need an IMU to provide basic positioning information. The system can determine your robot's exact location in an undefined environment. The SLAM process is a complex one and many back-end solutions exist. Whatever option you choose to implement a successful SLAM, it requires a constant interaction between the range measurement device and the software that extracts data, as well as the robot or vehicle. It is a dynamic process with a virtually unlimited variability. As the robot moves and around, it adds new scans to its map. The SLAM algorithm analyzes these scans against the previous ones using a process called scan matching. This assists in establishing loop closures. When a loop closure is identified it is then the SLAM algorithm makes use of this information to update its estimated robot trajectory. The fact that the environment can change in time is another issue that complicates 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 location it may have trouble finding the two points on its map. This is where the handling of dynamics becomes crucial and is a common characteristic of the modern Lidar SLAM algorithms. SLAM systems are extremely efficient at navigation and 3D scanning despite these challenges. It is especially useful in environments that don't allow the robot to rely on GNSS-based position, such as an indoor factory floor. It's important to remember that even a properly configured SLAM system may experience mistakes. It is vital to be able to detect these issues and comprehend how they affect the SLAM process in order to fix them. Mapping The mapping function builds an image of the robot's surrounding which includes the robot, its wheels and actuators and everything else that is in its field of view. This map is used for localization, route planning and obstacle detection. This is an area in which 3D lidars are particularly helpful because they can be effectively treated as a 3D camera (with one scan plane). Map building is a time-consuming process however, it is worth it in the end. The ability to build a complete and coherent map of a robot's environment allows it to navigate with high precision, and also over obstacles. As a rule, the higher the resolution of the sensor then the more accurate will be the map. However it is not necessary for all robots to have high-resolution maps: for example floor sweepers might not need the same degree of detail as a industrial robot that navigates factories with huge facilities. For this reason, there are a number of different mapping algorithms to use with LiDAR sensors. Cartographer is a popular algorithm that uses a two phase pose graph optimization technique. It adjusts for drift while maintaining an unchanging global map. It is especially efficient when combined with odometry data. Another alternative is GraphSLAM that employs a system of linear equations to model constraints of a graph. The constraints are represented as an O matrix and an X vector, with each vertice of the O matrix representing a distance to a landmark on the X vector. A GraphSLAM Update is a series subtractions and additions to these matrix elements. The result is that all O and X Vectors are updated in order to account for the new observations made by the robot. Another efficient mapping algorithm is SLAM+, which combines mapping and odometry using an Extended Kalman Filter (EKF). The EKF updates not only the uncertainty in the robot's current location, but also the uncertainty in the features drawn by the sensor. The mapping function will utilize this information to estimate its own location, 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 get to its desired point. It makes use of sensors such as digital cameras, infrared scanners, laser radar and sonar to detect its environment. It also makes use of an inertial sensor to measure its position, speed and the direction. These sensors help it navigate in a safe and secure manner and prevent collisions. A range sensor is used to gauge the distance between an obstacle and a robot. The sensor can be attached to the vehicle, the robot, or a pole. It is crucial to keep in mind that the sensor is affected by a variety of elements, including wind, rain and fog. It is crucial to calibrate the sensors prior each use. The results of the eight neighbor cell clustering algorithm can be used to determine static obstacles. This method is not very accurate because of the occlusion caused by the distance between the laser lines and the camera's angular speed. To address this issue, a technique of multi-frame fusion has been used to increase the accuracy of detection of static obstacles. The technique of combining roadside camera-based obstruction detection with the vehicle camera has proven to increase data processing efficiency. It also allows the possibility of redundancy for other navigational operations, like path planning. The result of this technique is a high-quality image of the surrounding environment that is more reliable than a single frame. In outdoor tests, the method was compared against other obstacle detection methods such as YOLOv5 monocular ranging, VIDAR. The results of the test revealed that the algorithm was able correctly identify the height and location of an obstacle, in addition to its tilt and rotation. It was also able detect the color and size of an object. The method also showed excellent stability and durability even in the presence of moving obstacles.