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LiDAR Robot Navigation
LiDAR robots navigate using the combination of localization and mapping, as well as path planning. This article will explain the concepts and demonstrate how they work by using a simple example where the robot reaches a goal within a plant row.
LiDAR sensors have modest power requirements, allowing them to prolong a robot's battery life and reduce the amount of raw data required for localization algorithms. This enables more variations of the SLAM algorithm without overheating the GPU.
LiDAR Sensors
The sensor is at the center of the Lidar system. It emits laser pulses into the environment. These light pulses strike objects and bounce back to the sensor at various angles, based on the composition of the object. The sensor determines how long it takes each pulse to return and then utilizes that information to calculate distances. The sensor is typically mounted on a rotating platform permitting it to scan the entire surrounding area at high speed (up to 10000 samples per second).
LiDAR sensors are classified based on whether they're designed for applications in the air or on land. Airborne lidars are usually mounted on helicopters or an unmanned aerial vehicles (UAV). Terrestrial LiDAR systems are usually mounted on a static robot platform.
To accurately measure distances, the sensor must be able to determine the exact location of the robot. This information is typically captured by a combination of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are employed by LiDAR systems to calculate the exact position of the sensor within the space and time. This information is then used to create a 3D model of the surrounding.
LiDAR scanners are also able to recognize different types of surfaces which is especially useful when mapping environments that have dense vegetation. For instance, if the pulse travels through a forest canopy it is common for it to register multiple returns. The first return is usually attributed to the tops of the trees while the second one is attributed to the ground's surface. If the sensor records these pulses separately and is referred to as discrete-return LiDAR.
The Discrete Return scans can be used to study surface structure. For instance, a forest region may result in one or two 1st and 2nd return pulses, with the final big pulse representing the ground. The ability to separate these returns and record them as a point cloud allows for the creation of detailed terrain models.
Once a 3D model of the environment is constructed the robot will be able to use this data to navigate. This process involves localization and creating a path to reach a navigation "goal." It also involves dynamic obstacle detection. This process detects new obstacles that were not present in the original map and updates the path plan accordingly.
SLAM Algorithms
SLAM (simultaneous localization and mapping) is an algorithm that allows your best robot vacuum with lidar to create an outline of its surroundings and then determine the location of its position in relation to the map. Engineers use the information to perform a variety of tasks, such as path planning and obstacle identification.
To enable SLAM to function it requires sensors (e.g. laser or camera) and a computer running the appropriate software to process the data. Also, you need an inertial measurement unit (IMU) to provide basic information about your position. The result is a system that can accurately track the location of your robot in a hazy environment.
The SLAM process is extremely complex and many back-end solutions exist. Whatever solution you choose, a successful SLAM system requires a constant interaction between the range measurement device, the software that extracts the data, and the robot or vehicle itself. This is a dynamic process with almost infinite variability.
As the robot moves about, it adds new scans to its map. The SLAM algorithm then compares these scans to earlier ones using a process known as scan matching. This allows loop closures to be created. The SLAM algorithm updates its robot's estimated trajectory when a loop closure has been identified.
The fact that the surrounding can change over time is a further factor that makes it more difficult for SLAM. For instance, if your robot walks through an empty aisle at one point and is then confronted by pallets at the next point, it will have difficulty matching these two points in its map. This is where handling dynamics becomes important, and this is a typical feature of modern Lidar SLAM algorithms.
SLAM systems are extremely effective in 3D scanning and navigation despite these limitations. It is particularly beneficial in situations where the robot isn't able to rely on GNSS for its positioning, such as an indoor factory floor. It is crucial to keep in mind that even a properly configured SLAM system can be prone to errors. It is vital to be able to detect these flaws and understand how they impact the SLAM process in order to correct them.
Mapping
The mapping function creates a map for a robot's environment. This includes the robot, its wheels, actuators and everything else that falls within its vision field. This map is used to aid in the localization of the robot, route planning and obstacle detection. This is a field in which 3D Lidars can be extremely useful because they can be used as a 3D Camera (with a single scanning plane).
The process of building maps takes a bit of time however, the end result pays off. The ability to create an accurate, complete map of the robot's environment allows it to conduct high-precision navigation as well as navigate around obstacles.
As a general rule of thumb, the higher resolution the sensor, the more accurate the map will be. Not all Autonomous cleaning robots require high-resolution maps. For example floor sweepers might not require the same level of detail as an industrial robotic system navigating large factories.
There are a variety of mapping algorithms that can be used with LiDAR sensors. Cartographer is a very popular algorithm that employs a two phase pose graph optimization technique. It corrects for drift while maintaining a consistent global map. It is especially useful when paired with odometry.
GraphSLAM is a different option, that uses a set linear equations to model the constraints in diagrams. The constraints are represented by an O matrix, and a vector X. Each vertice in the O matrix contains a distance from the X-vector's landmark. A GraphSLAM Update is a series of subtractions and additions to these matrix elements. The result is that both the O and X vectors are updated to take into account the latest observations made by the vacuum robot with lidar.
Another efficient mapping algorithm is SLAM+, which combines mapping and odometry using an Extended Kalman Filter (EKF). The EKF changes the uncertainty of the robot's position as well as the uncertainty of the features that were recorded by the sensor. This information can be used by the mapping function to improve its own estimation of its location and to update the map.
Obstacle Detection
A robot needs to be able to sense its surroundings in order to avoid obstacles and reach its goal point. It employs sensors such as digital cameras, infrared scans laser radar, and sonar to detect the environment. It also makes use of an inertial sensors to determine its speed, location and the direction. These sensors assist it in navigating in a safe and secure manner and prevent collisions.
A range sensor is used to determine the distance between a robot and an obstacle. The sensor can be mounted to the robot, a vehicle, or a pole. It is important to keep in mind that the sensor could be affected by a variety of factors such as wind, rain and fog. Therefore, it is essential to calibrate the sensor before every use.
The results of the eight neighbor cell clustering algorithm can be used to identify static obstacles. However, this method has a low accuracy in detecting because of the occlusion caused by the distance between the different laser lines and the angular velocity of the camera which makes it difficult to detect static obstacles in one frame. To overcome this problem, a method called multi-frame fusion has been used to improve the detection accuracy of static obstacles.
The technique of combining roadside camera-based obstruction detection with the vehicle camera has been proven to increase the efficiency of data processing. It also allows the possibility of redundancy for other navigational operations such as the planning of a path. This method provides an accurate, high-quality image of the environment. In outdoor comparison experiments the method was compared to other methods of obstacle detection like YOLOv5, monocular ranging and VIDAR.
The results of the test revealed that the algorithm was able to correctly identify the position and height of an obstacle, as well as its rotation and tilt. It also showed a high ability to determine the size of obstacles and its color. The method also exhibited excellent stability and durability, even when faced with moving obstacles.
