Gabrielle Sifford спросил 7 месяцев назад
LiDAR Robot Navigation
LiDAR robots move using a combination of localization, mapping, as well as path planning. This article will outline the concepts and show how they work by using an example in which the robot reaches an objective within a row of plants.
LiDAR sensors are relatively low power requirements, allowing them to prolong the life of a robot’s battery and reduce the amount of raw data required for localization algorithms. This allows for more iterations of the SLAM algorithm without overheating the GPU.
LiDAR Sensors
The central component of lidar systems is its sensor that emits pulsed laser light into the surrounding. 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 determine distances. Sensors are placed on rotating platforms that allow them to scan the surrounding area quickly and at high speeds (10000 samples per second).
LiDAR sensors are classified based on whether they are designed for applications in the air or on land. Airborne lidars are often mounted on helicopters or an unmanned aerial vehicle (UAV). Terrestrial LiDAR systems are typically placed on a stationary robot platform.
To accurately measure distances, the sensor needs to be aware of the precise location of the robot at all times. This information is recorded by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems utilize sensors to calculate the precise location of the sensor in space and time, which is later used to construct a 3D map of the environment.
LiDAR scanners can also detect different kinds of surfaces, which is particularly beneficial when mapping environments with dense vegetation. When a pulse passes a forest canopy, it will typically produce multiple returns. The first return is usually attributable to the tops of the trees while the second one is attributed to the ground’s surface. If the sensor records these pulses separately, it is called discrete-return LiDAR.
Discrete return scanning can also be useful in analyzing surface structure. For instance, a forest region could produce a sequence of 1st, 2nd, and 3rd returns, with a last large pulse that represents the ground. The ability to separate and store these returns in a point-cloud permits detailed terrain models.
Once a 3D map of the surroundings has been built and the robot has begun to navigate using this information. This involves localization, building a path to reach a goal for navigation and dynamic obstacle detection. This is the process that detects new obstacles that are not listed in the map’s original version and adjusts the path plan in line with the new obstacles.
SLAM Algorithms
SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its surroundings, and then determine its position in relation to the map. Engineers utilize the information for a number of tasks, such as path planning and obstacle identification.
To allow SLAM to function it requires a sensor (e.g. A computer with the appropriate software for processing the data as well as either a camera or laser are required. You also need an inertial measurement unit (IMU) to provide basic information about your position. The result is a system that will accurately track the location of your robot in an unknown environment.
The SLAM system is complex and there are a variety of back-end options. Whatever solution you choose to implement an effective SLAM, it requires a constant interaction between the range measurement device and the software that collects data and the robot or vehicle. This is a highly dynamic procedure that can have an almost infinite amount of variability.
When the robot moves, it adds scans to its map. The SLAM algorithm then compares these scans with the previous ones using a method called scan matching. This allows loop closures to be identified. The SLAM algorithm is updated with its robot’s estimated trajectory when loop closures are detected.
The fact that the surroundings changes over time is another factor that can make it difficult to use SLAM. If, for example, your robot is walking along an aisle that is empty at one point, and then encounters a stack of pallets at another point it may have trouble connecting the two points on its map. This is when handling dynamics becomes crucial, and this is a common feature of the modern lidar navigation SLAM algorithms.
SLAM systems are extremely effective at navigation and 3D scanning despite the challenges. It is particularly useful in environments that do not let the robot rely on GNSS-based position, such as an indoor factory floor. It’s important to remember that even a well-designed SLAM system may experience errors. To fix these issues it is crucial to be able to spot the effects of these errors and their implications on the SLAM process.
Mapping
The mapping function builds a map of the robot’s environment which includes the robot itself including its wheels and actuators and everything else that is in the area of view. The map is used for the localization, planning of paths and obstacle detection. This is an area in which 3D lidars can be extremely useful since they can be used like an actual 3D camera (with only one scan plane).
The process of creating maps takes a bit of time however the results pay off. The ability to create an accurate and complete map of a robot’s environment allows it to navigate with great precision, and also around obstacles.
As a rule, Quietest the greater the resolution of the sensor, then the more accurate will be the map. Not all robots require high-resolution maps. For example a floor-sweeping robot may not require the same level detail as an industrial robotic system that is navigating factories of a large size.
For this reason, there are a number of different mapping algorithms to use with LiDAR sensors. Cartographer is a popular algorithm that uses the two-phase pose graph optimization technique. It corrects for drift while maintaining an accurate global map. It is particularly efficient when combined with the odometry information.
GraphSLAM is another option, that uses a set linear equations to represent the constraints in diagrams. The constraints are represented by an O matrix, as well as an X-vector. Each vertice of the O matrix is a distance from the X-vector’s landmark. A GraphSLAM update consists of a series of additions and subtraction operations on these matrix elements which means that all of the O and X vectors are updated to account for new observations of the robot.
Another helpful mapping algorithm is SLAM+, which combines mapping and odometry using an Extended Kalman Filter (EKF). The EKF updates not only the uncertainty of the robot’s current location, 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 position and update the map.
Obstacle Detection
A robot needs to be able to sense its surroundings so it can avoid obstacles and reach its final point. It makes use of sensors such as digital cameras, infrared scanners sonar and laser radar to sense its surroundings. It also uses inertial sensors to monitor its position, speed and the direction. 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 a range sensor to determine the distance between the robot and the obstacles. The sensor can be placed on the robot, inside a vehicle or on poles. It is crucial to keep in mind that the sensor can be affected by various factors, such as rain, wind, and fog. It is crucial to calibrate the sensors prior to each use.
A crucial step in obstacle detection is identifying static obstacles. This can be done by 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 gap between the laser lines and [Redirect Only] the angular velocity of the camera which makes it difficult to recognize static obstacles in a single frame. To overcome this problem, multi-frame fusion was used to increase the accuracy of the static obstacle detection.
The method of combining roadside camera-based obstruction detection with vehicle camera has been proven to increase data processing efficiency. It also allows redundancy for other navigational tasks, like the planning of a path. The result of this method is a high-quality image of the surrounding area that is more reliable than one frame. In outdoor comparison tests, the method was compared to other methods of obstacle detection such as YOLOv5, monocular ranging and VIDAR.
The experiment results proved that the algorithm could accurately determine the height and location of an obstacle, as well as its tilt and rotation. It also had a good performance in identifying the size of an obstacle and its color. The method also exhibited excellent stability and durability, even when faced with moving obstacles.