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LiDAR Robot Navigation LiDAR robots move using the combination of localization and mapping, and also path planning. This article will explain the concepts and explain how they work using an example in which the robot is able to reach an objective within a plant row. LiDAR sensors have modest power requirements, allowing them to increase the battery life of a robot and reduce the raw data requirement for localization algorithms. This allows for a greater number of iterations of SLAM without overheating the GPU. LiDAR Sensors The core of a lidar system is its sensor which emits laser light in the surrounding. These light pulses bounce off objects around them at different angles depending on their composition. The sensor measures the time it takes for each return, which is then used to determine distances. Sensors are mounted on rotating platforms that allow them to scan the surroundings quickly and at high speeds (10000 samples per second). LiDAR sensors are classified based on their intended applications on land or in the air. Airborne lidar systems are commonly mounted on aircrafts, helicopters or UAVs. (UAVs). Terrestrial LiDAR systems are generally mounted on a stationary robot platform. To accurately measure distances the sensor must always know the exact location of the robot. This information is recorded using a combination of inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems make use of sensors to compute the precise location of the sensor in space and time, which is then used to create a 3D map of the surrounding area. LiDAR scanners are also able to recognize different types of surfaces which is especially beneficial for mapping environments with dense vegetation. For instance, when the pulse travels through a forest canopy, it will typically register several returns. Typically, the first return is associated with the top of the trees while the last return is related to the ground surface. If the sensor can record each peak of these pulses as distinct, it is referred to as discrete return LiDAR. The Discrete Return scans can be used to study the structure of surfaces. For instance, a forest region may produce a series of 1st and 2nd return pulses, with the final large pulse representing the ground. The ability to separate and record these returns as a point-cloud allows for detailed models of terrain. Once a 3D map of the surroundings has been built, the robot can begin to navigate using this data. This involves localization as well as creating a path to get to a navigation “goal.” It also involves dynamic obstacle detection. This is the method of identifying new obstacles that are not present in the map originally, and updating 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 location in relation to the map. Engineers use this information to perform a variety of tasks, including planning routes and obstacle detection. To allow SLAM to work, your robot must have an instrument (e.g. a camera or laser), and a computer running the appropriate software to process the data. You'll also require an IMU to provide basic positioning information. The result is a system that will accurately determine the location of your robot in an unspecified environment. The SLAM system is complicated and there are many different back-end options. Whatever solution you select the most effective SLAM system requires a constant interaction between the range measurement device, the software that extracts the data, and the robot or vehicle itself. This is a highly dynamic process that can have an almost endless amount of variance. As lidar based robot vacuum moves around the area, it adds new scans to its map. The SLAM algorithm will then compare these scans to the previous ones using a method known as scan matching. This assists in establishing loop closures. If a loop closure is identified when loop closure is detected, the SLAM algorithm uses this information to update its estimate of the robot's trajectory. Another factor that makes SLAM is the fact that the scene changes as time passes. If, for instance, 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 connecting the two points on its map. Handling dynamics are important in this case and are a characteristic of many modern Lidar SLAM algorithms. Despite these issues, a properly configured SLAM system is incredibly effective for navigation and 3D scanning. It is particularly useful in environments where the robot isn't able to rely on GNSS for positioning for positioning, like an indoor factory floor. However, it is important to keep in mind that even a properly configured SLAM system may have mistakes. To correct these mistakes it is crucial to be able to recognize the effects of these errors and their implications on the SLAM process. Mapping The mapping function builds a map of the robot's surrounding that includes the robot itself including its wheels and actuators as well as everything else within its field of view. The map is used for localization, path planning, and obstacle detection. This is an area where 3D Lidars are particularly useful because they can be regarded as a 3D Camera (with one scanning plane). Map building is a time-consuming process, but it pays off in the end. The ability to build a complete, consistent map of the surrounding area allows it to carry out high-precision navigation, as as navigate around 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 maps with high resolution. For instance floor sweepers may not need the same level of detail as a industrial robot that navigates factories of immense size. There are many different mapping algorithms that can be employed with LiDAR sensors. Cartographer is a popular algorithm that utilizes the two-phase pose graph optimization technique. It corrects for drift while ensuring an accurate global map. It is particularly effective when combined with odometry. GraphSLAM is another option, which uses a set of linear equations to represent the constraints in a diagram. The constraints are modeled as an O matrix and a X vector, with each vertex of the O matrix containing the distance to a landmark on the X vector. A GraphSLAM Update is a series additions and subtractions on these matrix elements. The result is that both the O and X Vectors are updated to reflect the latest 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 not only the uncertainty in the robot's current position but also the uncertainty in the features recorded by the sensor. This information can be utilized by the mapping function to improve its own estimation of its location, and also to update the map. Obstacle Detection A robot needs to be able to perceive its environment to avoid obstacles and reach its goal. It utilizes sensors such as digital cameras, infrared scanners, sonar and laser radar to sense its surroundings. Additionally, it utilizes inertial sensors to measure its speed, position and orientation. These sensors assist it in navigating in a safe and secure manner and prevent collisions. A range sensor is used to measure the distance between the robot and the obstacle. The sensor can be attached to the robot, a vehicle or a pole. It is important to keep in mind that the sensor may be affected by various elements, including wind, rain, and fog. It is important to calibrate the sensors before every use. The results of the eight neighbor cell clustering algorithm can be used to determine static obstacles. However, this method is not very effective in detecting obstacles due to the occlusion caused by the distance between the different laser lines and the angular velocity of the camera which makes it difficult to recognize static obstacles within a single frame. To address this issue multi-frame fusion was employed to improve the accuracy of static obstacle detection. The method of combining roadside camera-based obstacle detection with a vehicle camera has shown to improve data processing efficiency. It also provides redundancy for other navigational tasks like path planning. The result of this technique is a high-quality picture of the surrounding area that is more reliable than a single frame. In outdoor comparison tests the method was compared against other methods of obstacle detection like YOLOv5 monocular ranging, VIDAR. The experiment results proved that the algorithm could accurately determine the height and position of obstacles as well as its tilt and rotation. It also had a good ability to determine the size of obstacles and its color. The method also demonstrated excellent stability and durability even when faced with moving obstacles.