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LiDAR Robot Navigation LiDAR robots navigate by using a combination of localization and mapping and also path planning This article will explain the concepts and explain how they work by using a simple example where the robot reaches a goal within a row of plants LiDAR sensors are lowpower devices which can extend the battery life of robots and decrease the amount of raw data needed to run localization algorithms This allows for more iterations of SLAM without overheating the GPU LiDAR Sensors The heart of a lidar system is its sensor that emits pulsed laser light into the surrounding The light waves bounce off the surrounding objects in different angles based on their composition The sensor records the amount of time it takes to return each time and uses this information to calculate 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 are classified according to their intended airborne or terrestrial application Airborne lidar systems are usually connected to aircrafts helicopters or UAVs UAVs Terrestrial LiDAR is usually installed on a robotic platform that is stationary To accurately measure distances the sensor must always know the exact location of the robot This information is recorded by a combination inertial measurement unit IMU GPS and timekeeping electronic robot vacuum with lidar use sensors to compute the exact location of the sensor in time and space which is then used to build up a 3D map of the surroundings LiDAR scanners can also identify different types of surfaces which is especially useful when mapping environments with dense vegetation For instance if a pulse passes through a forest canopy it is likely to register multiple returns The first return is attributed to the top of the trees and the last one is associated with the ground surface If the sensor records these pulses separately this is known as discretereturn LiDAR Discrete return scanning can also be useful for analyzing the structure of surfaces For instance a forested region could produce an array of 1st 2nd and 3rd returns with a final large pulse that represents the ground The ability to separate these returns and store them as a point cloud allows for the creation of detailed terrain models Once a 3D map of the surroundings has been built the robot can begin to navigate using this information This process involves localization constructing a path to reach a goal for navigation and dynamic obstacle detection This is the process that identifies new obstacles not included in the original map and updates the path plan accordingly SLAM Algorithms SLAM simultaneous localization and mapping is an algorithm that allows your robot to build an image of its surroundings and then determine the position of the robot in relation to the map Engineers make use of this information to perform a variety of tasks including planning routes and obstacle detection To enable SLAM to function the robot needs an instrument eg a camera or laser and a computer with the right software to process the data You also need an inertial measurement unit IMU to provide basic information on your location The result is a system that will precisely track the position of your robot in an unknown environment The SLAM process is extremely complex and many backend solutions are available No matter which solution you choose for an effective SLAM is that it requires constant communication between the range measurement device and the software that extracts data as well as the vehicle or robot This is a dynamic process with a virtually unlimited variability As the robot moves about it adds new scans to its map The SLAM algorithm compares these scans with prior ones using a process called scan matching This aids in establishing loop closures The SLAM algorithm updates its estimated robot trajectory when the loop has been closed detected Another factor that complicates SLAM is the fact that the scene changes in time If for example your robot is walking along an aisle that is empty at one point but then comes across a pile of pallets at a different point it may have difficulty matching the two points on its map Handling dynamics are important in this scenario and they are a part of a lot of modern Lidar SLAM algorithms SLAM systems are extremely efficient in 3D scanning and navigation despite these limitations It is especially useful in environments that do not permit the robot to depend on GNSS for position such as an indoor factory floor However it is important to note that even a welldesigned SLAM system may have errors 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 outline of the robots surrounding that includes the robot itself its wheels and actuators as well as everything else within its field of view This map is used for the localization of the robot route planning and obstacle detection This is an area where 3D lidars are particularly helpful because they can be utilized as an actual 3D camera with only one scan plane The process of creating maps can take some time but the results pay off The ability to build an accurate complete map of the surrounding area allows it to carry out highprecision navigation as as navigate around obstacles The higher the resolution of the sensor then the more accurate will be the map Not all robots require maps with high resolution For example floor sweepers might not require the same level of detail as an industrial robotics system that is navigating factories of a large size For this reason there are a variety of different mapping algorithms for use with LiDAR sensors One popular algorithm is called Cartographer which utilizes a twophase pose graph optimization technique to correct for drift and maintain an accurate global map It is especially beneficial when used in conjunction with Odometry data GraphSLAM is a different option that uses a set linear equations to represent constraints in diagrams The constraints are modeled as an O matrix and a the X vector with every vertex of the O matrix containing the distance to a landmark on the X vector A GraphSLAM update consists of the addition and subtraction operations on these matrix elements and the result is that all of the X and O vectors are updated to account for new robot observations Another efficient mapping algorithm is SLAM which combines the use of odometry with mapping using an Extended Kalman Filter EKF The EKF changes the uncertainty of the robots location as well as the uncertainty of the features that were drawn by the sensor This information can be used by the mapping function to improve its own estimation of its position and update the map Obstacle Detection A robot needs to be able to see its surroundings to avoid obstacles and get to its destination It uses sensors like digital cameras infrared scanners sonar and laser radar to sense its surroundings In addition it uses inertial sensors to measure its speed and position as well as its orientation These sensors assist it in navigating in a safe way and prevent collisions A range sensor is used to measure the distance between a robot and an obstacle The sensor can be mounted on the robot inside an automobile or on the pole It is crucial to keep in mind that the sensor can be affected by various elements including rain wind and fog It is important to calibrate the sensors before each use The results of the eight neighbor cell clustering algorithm can be used to identify static obstacles This method isnt particularly accurate because of the occlusion created by the distance between the laser lines and the cameras angular velocity To overcome this issue multiframe fusion was used to increase the effectiveness of static obstacle detection The method of combining roadside unitbased and obstacle detection by a vehicle camera has been shown to improve the data processing efficiency and reserve redundancy for subsequent navigation operations such as path planning This method creates a highquality reliable image of the environment The method has been tested with other obstacle detection methods such as YOLOv5 VIDAR YOLOv5 as well as monocular ranging in outdoor tests of comparison The results of the experiment showed that the algorithm was able accurately identify the position and height of an obstacle as well as its rotation and tilt It was also able determine the color and size of an object The algorithm was also durable and reliable even when obstacles moved