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LiDAR Robot Navigation LiDAR robots navigate using a combination of localization and mapping, as well as path planning. This article will introduce the concepts and explain how they function using an easy example where the robot is able to reach an objective within a row of plants. LiDAR sensors have low power requirements, allowing them to extend a robot's battery life and reduce the need for raw data for localization algorithms. This allows for a greater number of iterations of SLAM without overheating the GPU. LiDAR Sensors The sensor is the core of a Lidar system. It releases laser pulses into the surrounding. These light pulses bounce off objects around them at different angles based on their composition. The sensor records the amount of time required for each return and then uses it to determine 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 based on whether they're intended for use in the air or on the ground. Airborne lidar systems are typically attached to helicopters, aircraft, or UAVs. (UAVs). Terrestrial LiDAR is usually installed on a robotic platform that is stationary. To accurately measure you can try this out , the sensor needs to be aware of the precise location of the robot at all times. This information is usually gathered through a combination of inertial measuring units (IMUs), GPS, and time-keeping electronics. LiDAR systems make use of these sensors to compute the exact location of the sensor in space and time. This information is later used to construct an image of 3D of the environment. LiDAR scanners are also able to recognize different types of surfaces and types of surfaces, which is particularly beneficial for mapping environments with dense vegetation. For instance, when a pulse passes through a canopy of trees, it will typically register several returns. Typically, the first return is attributable to the top of the trees and the last one is associated with the ground surface. If the sensor captures each peak of these pulses as distinct, this is referred to as discrete return LiDAR. The Discrete Return scans can be used to analyze surface structure. For instance, a forest area could yield an array of 1st, 2nd and 3rd return, with a final large pulse representing the ground. The ability to separate and store these returns as a point cloud allows for precise models of terrain. Once a 3D map of the surrounding area has been created and the robot has begun to navigate based on this data. This involves localization as well as creating a path to get to a navigation “goal.” It also involves dynamic obstacle detection. The latter is the process of identifying obstacles that aren't present on the original map and adjusting the path plan accordingly. SLAM Algorithms SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to create an image of its surroundings and then determine where it is relative to the map. Engineers utilize the data for a variety of tasks, such as planning a path and identifying obstacles. For SLAM to function it requires an instrument (e.g. a camera or laser), and a computer that has the right software to process the data. Also, you need an inertial measurement unit (IMU) to provide basic positional information. The result is a system that can precisely track the position of your robot in an unknown environment. The SLAM process is complex and many back-end solutions exist. Whatever solution you choose, a successful SLAM system requires a constant interplay between the range measurement device, the software that extracts the data and the robot or vehicle itself. This is a highly dynamic process that has an almost unlimited amount of variation. As the robot moves around, it adds new scans to its map. The SLAM algorithm will then compare these scans to the previous ones using a method called scan matching. This assists in establishing loop closures. The SLAM algorithm adjusts its estimated robot trajectory once a loop closure has been identified. Another factor that makes SLAM is the fact that the scene changes in time. For example, if your robot is walking through an empty aisle at one point, and then comes across pallets at the next point, it will have difficulty connecting these two points in its map. This is where handling dynamics becomes crucial, and this is a common characteristic of modern Lidar SLAM algorithms. Despite these difficulties, a properly-designed SLAM system is extremely efficient for navigation and 3D scanning. It is especially beneficial in situations where the robot isn't able to rely on GNSS for positioning for positioning, like an indoor factory floor. It is crucial to keep in mind that even a properly configured SLAM system may experience errors. To correct these mistakes it is essential to be able to spot them and comprehend their impact on the SLAM process. Mapping The mapping function builds an image of the robot's environment that includes the robot, its wheels and actuators, and everything else in the area of view. The map is used for location, route planning, and obstacle detection. This is an area where 3D lidars are extremely helpful, as they can be used as an actual 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 and complete map of the robot's surroundings allows it to navigate with high precision, and also over obstacles. As a general rule of thumb, the greater resolution of the sensor, the more accurate the map will be. However, not all robots need maps with high resolution. For instance floor sweepers may not require the same amount of detail as a industrial robot that navigates factories with huge facilities. For this reason, there are a variety of different mapping algorithms to use with LiDAR sensors. Cartographer is a well-known algorithm that employs a two-phase pose graph optimization technique. It corrects for drift while maintaining a consistent global map. It is especially useful when paired with the odometry. Another option is GraphSLAM which employs linear equations to represent the constraints of graph. The constraints are represented by an O matrix, as well as an the X-vector. Each vertice in the O matrix represents a distance from an X-vector landmark. A GraphSLAM Update is a series of additions and subtractions on these matrix elements. The result is that both the O and X Vectors are updated to account for the new observations made by the robot. SLAM+ is another useful mapping algorithm that combines odometry and 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 utilize this information to improve its own location, allowing it to update the base map. Obstacle Detection A robot must be able see its surroundings so that it can avoid obstacles and get to its goal. It makes use of sensors such as digital cameras, infrared scanners sonar and laser radar to determine its surroundings. Additionally, it employs inertial sensors to measure its speed and position, as well as its orientation. These sensors aid in navigation in a safe way and prevent collisions. A key element of this process is the detection of obstacles that involves the use of an IR range sensor to measure the distance between the robot and obstacles. The sensor can be positioned on the robot, inside the vehicle, or on a pole. It is crucial to keep in mind that the sensor could be affected by various elements, including rain, wind, and fog. Therefore, it is crucial to calibrate the sensor prior to every use. An important step in obstacle detection is the identification of static obstacles, which can be accomplished using the results of the eight-neighbor-cell clustering algorithm. However, this method has a low detection accuracy due to the occlusion caused by the gap between the laser lines and the angular velocity of the camera making it difficult to recognize static obstacles in a single frame. To solve this issue, a method of multi-frame fusion has been used to increase the accuracy of detection of static obstacles. The method of combining roadside unit-based as well as obstacle detection by a vehicle camera has been proven to increase the efficiency of data processing and reserve redundancy for future navigational operations, like path planning. This method provides a high-quality, reliable image of the environment. The method has been tested with other obstacle detection methods like YOLOv5, VIDAR, and monocular ranging in outdoor comparative tests. The results of the test proved that the algorithm could accurately identify the height and location of an obstacle as well as its tilt and rotation. It also had a great ability to determine the size of an obstacle and its color. The method also demonstrated solid stability and reliability, even in the presence of moving obstacles.