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LiDAR Robot NavigationLiDAR robot navigation is a complex combination of mapping, localization and path planning. This article will explain these concepts and show how they work together using an example of a robot achieving its goal in the middle of a row of crops.LiDAR sensors have modest power demands allowing them to increase the battery life of a robot and reduce the amount of raw data required for localization algorithms. This allows for more repetitions of SLAM without overheating the GPU.LiDAR SensorsThe core of lidar systems is its sensor that emits laser light in the environment. The light waves hit objects around and bounce back to the sensor at various angles, depending on the composition of the object. The sensor determines how long it takes each pulse to return and uses that information to calculate distances. Sensors are positioned on rotating platforms, which allow them to scan the surrounding area quickly and at high speeds (10000 samples per second).LiDAR sensors are classified according to their intended airborne or terrestrial application. Airborne lidar systems are usually attached to helicopters, aircraft, or UAVs. (UAVs). Terrestrial LiDAR systems are usually placed 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. These sensors are used by LiDAR systems to determine the exact location of the sensor in space and time. This information is then used to build a 3D model of the surrounding environment.LiDAR scanners are also able to recognize different types of surfaces and types of surfaces, which is particularly useful when mapping environments that have dense vegetation. For example, when an incoming pulse is reflected through a canopy of trees, it is likely to register multiple returns. The first one is typically attributed to the tops of the trees while the second is associated with the ground's surface. If the sensor records these pulses separately this is known as discrete-return LiDAR.The Discrete Return scans can be used to analyze surface structure. For example, a forest region may result in an array of 1st and 2nd returns with the final large pulse representing the ground. The ability to separate these returns and record them as a point cloud makes it possible for the creation of precise terrain models.Once a 3D model of environment is created the robot will be capable of using this information to navigate. This process involves localization, building an appropriate path to reach a navigation 'goal and dynamic obstacle detection. This process detects new obstacles that are not listed in the original map and then updates the plan of travel accordingly.SLAM AlgorithmsSLAM (simultaneous localization and mapping) is an algorithm that allows your robot to create an image of its surroundings and then determine the position of the robot relative to the map. Engineers use the data for a variety of tasks, such as the planning of routes and obstacle detection.To allow SLAM to function the robot needs a sensor (e.g. the laser or camera) and a computer that has the appropriate software to process the data. You'll also require an IMU to provide basic positioning information. The system will be able to track the precise location of your robot in a hazy environment.The SLAM system is complex and there are many different back-end options. Whatever option you choose to implement an effective SLAM is that it requires a constant interaction between the range measurement device and the software that extracts the data and the robot or vehicle. This is a highly dynamic process that can have an almost infinite amount of variability.As the robot moves about and around, it adds new scans to its map. The SLAM algorithm analyzes these scans against the previous ones using a process known as scan matching. This allows loop closures to be created. The SLAM algorithm is updated with its robot's estimated trajectory when the loop has been closed discovered.Another factor that complicates SLAM is the fact that the surrounding changes over time. If, for instance, your robot is walking along an aisle that is empty at one point, but 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 critical, and this is a typical feature of modern Lidar SLAM algorithms.SLAM systems are extremely efficient in 3D scanning and navigation despite the challenges. It is especially useful in environments where the robot can't rely on GNSS for positioning for positioning, like an indoor factory floor. lidar robot to remember that even a properly configured SLAM system can be prone to mistakes. To correct these mistakes, it is important to be able to spot them and understand their impact on the SLAM process.MappingThe mapping function creates a map of the robot's surroundings, which includes the robot, its wheels and actuators as well as everything else within its view. This map is used for localization, route planning and obstacle detection. This is an area where 3D Lidars can be extremely useful, since they can be treated as an 3D Camera (with one scanning plane).Map creation is a long-winded process but it pays off in the end. The ability to create a complete, consistent map of the robot's environment allows it to conduct high-precision navigation, as being able to navigate around obstacles.As a general rule of thumb, the greater resolution the sensor, more precise the map will be. However there are exceptions to the requirement for high-resolution maps. For example floor sweepers may not need the same amount of detail as an industrial robot that is navigating large factory facilities.To this end, there are a variety of different mapping algorithms that can be used with LiDAR sensors. One of the most popular algorithms is Cartographer, which uses two-phase pose graph optimization technique to adjust for drift and keep a uniform global map. It is particularly efficient when combined with odometry data.Another option is GraphSLAM that employs linear equations to model the constraints of graph. The constraints are modelled as an O matrix and an one-dimensional X vector, each vertice of the O matrix representing a distance to a landmark on the X vector. A GraphSLAM update consists of an array of additions and subtraction operations on these matrix elements and the result is that all of the X and O vectors are updated to accommodate new observations of the robot.Another helpful mapping algorithm is SLAM+, which combines the use of odometry with 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 mapped by the sensor. This information can be utilized by the mapping function to improve its own estimation of its position and update the map.Obstacle DetectionA robot should be able to see its surroundings to avoid obstacles and get to its goal. It makes use of sensors like digital cameras, infrared scans, sonar, laser radar and others to sense the surroundings. Additionally, it utilizes inertial sensors that measure its speed, position and orientation. These sensors aid in navigation in a safe manner and avoid collisions.A range sensor is used to determine the distance between the robot and the obstacle. The sensor can be placed on the robot, in a vehicle or on a pole. It is important to remember that the sensor is affected by a myriad of factors like rain, wind and fog. Therefore, it is crucial to calibrate the sensor prior every use.A crucial step in obstacle detection is identifying static obstacles, which can be accomplished using the results of the eight-neighbor-cell clustering algorithm. This method is not very precise due to the occlusion induced by the distance between the laser lines and the camera's angular speed. To address this issue multi-frame fusion was implemented to increase the accuracy of static obstacle detection.The technique of combining roadside camera-based obstruction detection with the vehicle camera has proven to increase the efficiency of data processing. It also reserves redundancy for other navigation operations, like the planning of a path. This method produces an image of high-quality and reliable of the environment. The method has been compared with other obstacle detection methods like YOLOv5, VIDAR, and monocular ranging in outdoor comparison experiments.The results of the test proved that the algorithm was able to correctly identify the height and location of an obstacle, as well as its rotation and tilt. It was also able determine the size and color of the object. The method also showed excellent stability and durability, even when faced with moving obstacles.