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LiDAR Robot Navigation

LiDAR robot navigation is a complicated combination of mapping, localization and path planning. This article will introduce the concepts and explain how they function using an easy example where the robot reaches a goal within a plant row.

LiDAR sensors are relatively low power requirements, allowing them to prolong the battery life of a robot and decrease the raw data requirement for localization algorithms. This allows for more iterations of SLAM without overheating the GPU.

LiDAR Sensors

The central component of lidar systems is their sensor, which emits laser light pulses into the environment. The light waves hit objects around and bounce back to the sensor at a variety of angles, based on the structure of the object. The sensor is able to measure the time it takes for each return, which is then used to determine distances. Sensors are mounted on rotating platforms, LiDAR robot navigation which allow them to scan the surrounding area quickly and at high speeds (10000 samples per second).

LiDAR sensors are classified by the type of sensor they are designed for applications on land or in the air. Airborne lidar systems are commonly attached to helicopters, aircraft or unmanned aerial vehicles (UAVs). Terrestrial LiDAR systems are generally placed on a stationary robot platform.

To accurately measure distances, the sensor must be aware of the exact location of the robot at all times. This information is typically captured using a combination of inertial measuring units (IMUs), GPS, and time-keeping electronics. These sensors are utilized by LiDAR systems to determine the precise position of the sensor within the space and time. The information gathered is used to create a 3D representation of the surrounding environment.

LiDAR scanners can also identify different kinds of surfaces, which is especially useful when mapping environments that have dense vegetation. For instance, when an incoming pulse is reflected through a forest canopy it will typically register several returns. The first return is attributable to the top of the trees while the final return is related to the ground surface. If the sensor can record each peak of these pulses as distinct, this is called discrete return LiDAR.

The Discrete Return scans can be used to analyze surface structure. For instance, a forest region may result in one or two 1st and 2nd return pulses, with the final large pulse representing bare ground. The ability to separate these returns and store them as a point cloud allows to create detailed terrain models.

Once an 3D model of the environment is created and the robot is capable of using this information to navigate. This process involves localization and making a path that will get to a navigation "goal." It also involves dynamic obstacle detection. This is the process of identifying new obstacles that aren't present in the original map, and adjusting 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 where it is in relation to the map. Engineers use the information to perform a variety of tasks, such as the planning of routes and obstacle detection.

To utilize SLAM the robot needs to have a sensor that provides range data (e.g. the laser or camera) and a computer running the right software to process the data. You'll also require an IMU to provide basic information about your position. The system can determine your robot's exact location in an unknown environment.

The SLAM process is complex and a variety of back-end solutions exist. No matter which solution you select for the success of SLAM it requires constant interaction between the range measurement device and the software that extracts the data and also the robot or vehicle. This is a dynamic procedure that is almost indestructible.

As the robot moves, it adds scans to its map. The SLAM algorithm will then compare these scans to the previous ones using a method called scan matching. This allows loop closures to be identified. When a loop closure has been discovered, the SLAM algorithm makes use of this information to update its estimate of the robot's trajectory.

The fact that the surroundings changes in time is another issue that can make it difficult to use SLAM. For instance, if a eufy RoboVac X8: Advanced Robot Vacuum Cleaner walks through an empty aisle at one point and is then confronted by pallets at the next location, it will have difficulty connecting these two points in its map. This is where the handling of dynamics becomes critical, and this is a typical feature of the modern Lidar SLAM algorithms.

Despite these issues however, a properly designed SLAM system can be extremely effective for navigation and 3D scanning. It is particularly useful in environments where the robot can't rely on GNSS for its 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. It is essential to be able to spot these flaws and understand how they affect the SLAM process to rectify them.

Mapping

The mapping function creates an image of the robot's environment, which includes the robot as well as its wheels and actuators as well as everything else within its view. The map is used to perform localization, path planning, and obstacle detection. This is an area where 3D lidars are particularly helpful since they can be effectively treated as an actual 3D camera (with a single scan plane).

The process of creating maps can take some time however, the end result pays off. The ability to create a complete and consistent map of the environment around a robot allows it to navigate with great precision, as well as around obstacles.

As a rule of thumb, the higher resolution the sensor, more precise the map will be. Not all robots require maps with high resolution. For example a floor-sweeping robot might not require the same level detail as an industrial robotics system operating in large factories.

This is why there are many different mapping algorithms for use with LiDAR sensors. One popular algorithm is called Cartographer, which uses a two-phase pose graph optimization technique to correct for drift and maintain a uniform global map. It is especially useful when used in conjunction with Odometry.

Another option is GraphSLAM which employs linear equations to model the constraints in a graph. The constraints are modeled as an O matrix and an X vector, with each vertice of the O matrix containing a distance to a landmark on the X vector. A GraphSLAM update is an array of additions and subtraction operations on these matrix elements with the end result being that all of the O and X vectors are updated to accommodate new information about 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 location, but also the uncertainty of the features that have been mapped by the sensor. This information can be used 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 see its surroundings to avoid obstacles and get to its desired point. It utilizes sensors such as digital cameras, infrared scanners, LiDAR robot navigation laser radar and sonar to sense its surroundings. It also uses inertial sensors to determine its speed, position and its orientation. These sensors assist it in navigating in a safe manner and prevent collisions.

One important part of this process is the detection of obstacles that consists of the use of sensors to measure the distance between the robot and the obstacles. The sensor can be mounted to the vehicle, the robot or a pole. It is important to keep in mind that the sensor may be affected by a variety of factors, such as rain, wind, and fog. It is important to calibrate the sensors before each use.

The most important aspect of 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 accuracy in detecting due to the occlusion caused by the spacing between different laser lines and the speed of the camera's angular velocity which makes it difficult to recognize static obstacles in one frame. To solve this issue, a technique of multi-frame fusion has been employed to increase the accuracy of detection of static obstacles.

The method of combining roadside camera-based obstruction detection with a vehicle camera has proven to increase data processing efficiency. It also allows redundancy for other navigation operations such as path planning. The result of this method 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 with other obstacle detection methods like YOLOv5 monocular ranging, and VIDAR.

The results of the test proved that the algorithm was able to accurately identify the height and location of an obstacle, in addition to its rotation and tilt. It was also able determine the color and size of the object. The method was also robust and reliable even when obstacles moved.