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LiDAR Robot Navigation LiDAR robot navigation is a complicated combination of localization mapping and path planning This article will present these concepts and show how they work together using an easy example of the robot achieving its goal in a row of crop LiDAR sensors have low power demands allowing them to extend the battery life of a robot and decrease the amount of raw data required for localization algorithms This allows for a greater number of iterations of SLAM without overheating the GPU LiDAR Sensors The central component of lidar systems is their sensor that emits pulsed laser light into the environment These pulses bounce off the surrounding objects in different angles based on their composition The sensor measures the time it takes for each return which is then used to calculate distances The sensor is typically placed on a rotating platform allowing it to quickly scan the entire area at high speed up to 10000 samples per second LiDAR sensors are classified based on the type of sensor they are designed for applications on land or in the air Airborne lidar systems are commonly connected to aircrafts helicopters or UAVs UAVs Terrestrial LiDAR systems are typically mounted on a static robot platform To accurately measure distances the sensor needs to be aware of the exact location of the robot at all times This information is recorded by a combination of an inertial measurement unit IMU GPS and timekeeping electronic LiDAR systems utilize these sensors to compute the precise location of the sensor in time and space which is then used to build up a 3D map of the surrounding area LiDAR scanners can also detect different types of surfaces which is especially useful when mapping environments with dense vegetation When a pulse crosses a forest canopy it will usually generate multiple returns Usually the first return is attributable to the top of the trees while the final return is attributed to the ground surface If the sensor records each peak of these pulses as distinct this is referred to as discrete return LiDAR Distinte return scans can be used to study surface structure For example forests can result in a series of 1st and 2nd return pulses with the last one representing the ground The ability to divide these returns and save them as a point cloud makes it possible for the creation of detailed terrain models Once a 3D model of environment is built and the robot is equipped to navigate This involves localization constructing the path needed to reach a goal for navigation and dynamic obstacle detection This is the process that detects new obstacles that were not present in the map that was created and adjusts the path plan according to the new obstacles SLAM Algorithms SLAM simultaneous localization and mapping is an algorithm that allows your robot to build an image of its surroundings and then determine where it is in relation to the map Engineers make use of this data for a variety of purposes including path planning and obstacle identification To enable SLAM to function it requires an instrument eg a camera or laser and a computer that has the appropriate software to process the data Youll also require an IMU to provide basic positioning information The result is a system that can precisely track the position of your robot in a hazy environment his comment is here is complex and there are a variety of backend options Regardless of which solution you choose the most effective SLAM system requires a constant interaction between the range measurement device and the software that extracts the data and the vehicle or robot This is a highly dynamic procedure that is prone to an endless amount of variance When the robot moves it adds scans to its map The SLAM algorithm compares these scans with previous ones by using a process known as scan matching This aids in establishing loop closures The SLAM algorithm updates its estimated robot trajectory when the loop has been closed identified The fact that the surrounding can change over time is a further factor that can make it difficult to use SLAM For example if your robot is walking down an empty aisle at one point and is then confronted by pallets at the next spot it will have difficulty connecting these two points in its map This is where the handling of dynamics becomes important and this is a typical characteristic of modern Lidar SLAM algorithms Despite these difficulties a properly configured SLAM system is extremely efficient for navigation and 3D scanning It is particularly useful in environments that dont allow the robot to rely on GNSS positioning like an indoor factory floor It is important to keep in mind that even a properly configured SLAM system may experience mistakes To correct these errors it is essential to be able to spot them and understand their impact on the SLAM process Mapping The mapping function builds an outline of the robots environment that includes the robot as well as its wheels and actuators as well as everything else within the area of view This map is used for localization route planning and obstacle detection This is a domain where 3D Lidars can be extremely useful as they can be used as an 3D Camera with a single scanning plane Map creation can be a lengthy process but it pays off in the end The ability to create a complete and consistent map of the environment around a robot allows it to navigate with great precision and also around obstacles As a general rule of thumb the higher resolution of the sensor the more accurate the map will be Not all robots require highresolution maps For instance a floorsweeping robot might not require the same level of detail as a robotic system for industrial use navigating large factories For this reason there are many different mapping algorithms for use with LiDAR sensors Cartographer is a popular algorithm that uses a two phase pose graph optimization technique It corrects for drift while maintaining an accurate global map It is particularly useful when used in conjunction with Odometry GraphSLAM is a second option which utilizes a set of linear equations to represent constraints in a diagram The constraints are modeled as an O matrix and an the X vector with every vertex of the O matrix representing a distance to a landmark on the X vector A GraphSLAM Update is a series of subtractions and additions to these matrix elements The result is that all O and X vectors are updated to take into account the latest observations made by the robot Another useful mapping algorithm is SLAM which combines odometry and mapping using an Extended Kalman Filter EKF The EKF alters the uncertainty of the robots location as well as the uncertainty of the features that were 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 should be able to see its surroundings to overcome obstacles and reach its goal It employs sensors such as digital cameras infrared scans sonar laser radar and others to determine the surrounding Additionally it utilizes inertial sensors to determine its speed and position as well as its orientation These sensors help it navigate in a safe and secure manner 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 in the vehicle or on poles It is important to keep in mind that the sensor can be affected by a variety of elements including wind rain and fog Therefore it is important to calibrate the sensor prior to each use The results of the eight neighbor cell clustering algorithm can be used to detect static obstacles This method isnt very precise due to the occlusion caused by the distance between laser lines and the cameras angular velocity To overcome this issue multiframe fusion was implemented to increase the accuracy of static obstacle detection The technique of combining roadside camerabased obstacle detection with a vehicle camera has proven to increase data processing efficiency It also allows redundancy for other navigational tasks like planning a path The result of this method is a highquality image of the surrounding environment that is more reliable than one frame The method has been compared with other obstacle detection methods such as YOLOv5 VIDAR YOLOv5 as well as monocular ranging in outdoor comparison experiments The results of the test 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 performance in detecting the size of the obstacle and its color The method also demonstrated good stability and robustness even when faced with moving obstacles