LiDAR Robot Navigation
LiDAR robots navigate by using the combination of localization and mapping, as well as path planning. This article will explain these concepts and show how they work together using an example of a robot achieving its goal in the middle of a row of crops.
LiDAR sensors have modest 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 SLAM without overheating the GPU.
LiDAR Sensors
The sensor is the heart of the Lidar system. It emits laser pulses 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 measures the time it takes for each return and uses this information to calculate distances. The sensor is typically mounted on a rotating platform which allows it to scan the entire surrounding area at high speed (up to 10000 samples per second).
LiDAR sensors can be classified based on whether they're designed for airborne application or terrestrial application. Airborne lidars are often connected to helicopters or an unmanned aerial vehicles (UAV). Terrestrial LiDAR is usually mounted 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 captured by a combination of an inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems use sensors to calculate the exact location of the sensor in space and time, which is later used to construct a 3D map of the environment.
LiDAR scanners are also able to identify different kinds of surfaces, which is particularly useful when mapping environments that have dense vegetation. When a pulse passes a forest canopy, it is likely to produce multiple returns. vacuum robot lidar is associated with the top of the trees, while the last return is related to the ground surface. If the sensor records each peak of these pulses as distinct, it is called discrete return LiDAR.
Discrete return scanning can also be helpful in studying surface structure. For instance, a forested area could yield the sequence of 1st 2nd and 3rd returns with a final, large pulse representing the ground. The ability to separate these returns and record them as a point cloud allows to create detailed terrain models.
Once an 3D model of the environment is constructed, the robot will be able to use this data to navigate. This involves localization, constructing an appropriate path to reach a goal for navigation and dynamic obstacle detection. The latter is the process of identifying obstacles that are not present in the map originally, and updating the path plan accordingly.
SLAM Algorithms
SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its environment and then determine its location relative to that map. Engineers use the data for a variety of purposes, including path planning and obstacle identification.
For SLAM to function it requires sensors (e.g. a camera or laser) and a computer that has the right software to process the data. You'll also require an IMU to provide basic positioning information. The system can determine your robot's exact location in a hazy environment.
The SLAM process is extremely complex, and many different back-end solutions exist. Whatever solution you select for a successful SLAM is that it 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 can have an almost infinite amount of variability.
As the robot moves, it adds scans to its map. The SLAM algorithm then compares these scans with previous ones using a process called scan matching. This allows loop closures to be established. When a loop closure has been detected, the SLAM algorithm uses this information to update its estimate of the robot's trajectory.
The fact that the surroundings can change in time is another issue that makes it more difficult for SLAM. If, for example, your robot is walking down an aisle that is empty at one point, but it comes across a stack of pallets at a different point it may have trouble finding the two points on its map. This is where the handling of dynamics becomes important, and this is a common feature of modern Lidar SLAM algorithms.
SLAM systems are extremely efficient at navigation and 3D scanning despite these limitations. It is especially beneficial in situations that don't depend on GNSS to determine its position for positioning, like an indoor factory floor. However, it is important to note that even a well-designed SLAM system can be prone to errors. It is vital to be able to spot these flaws and understand how they affect the SLAM process in order to fix them.
Mapping
The mapping function creates an image of the robot's environment, which includes the robot including its wheels and actuators as well as everything else within its field of view. This map is used for localization, path planning and obstacle detection. This is a domain where 3D Lidars are especially helpful because they can be treated as a 3D Camera (with one scanning plane).
The process of building maps can take some time however, the end result pays off. The ability to build a complete, consistent map of the robot's surroundings allows it to carry out high-precision navigation, as well being able to navigate around obstacles.
As a general rule of thumb, the higher resolution the sensor, more accurate the map will be. However, not all robots need high-resolution maps. For example, a floor sweeper may not need the same degree of detail as an industrial robot that is navigating large factory facilities.
To this end, there are a number of different mapping algorithms to use with LiDAR sensors. Cartographer is a very popular algorithm that utilizes the two-phase pose graph optimization technique. It corrects for drift while ensuring an accurate global map. It is especially useful when combined with odometry.
GraphSLAM is another option, that uses a set linear equations to model the constraints in diagrams. The constraints are represented as an O matrix and an X vector, with each vertex of the O matrix representing the distance to a point 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 in order to take into account the latest observations made by the robot.
Another efficient mapping algorithm is SLAM+, which combines mapping and odometry using an Extended Kalman Filter (EKF). The EKF updates not only the uncertainty in the robot's current position but also the uncertainty of the features that have been drawn 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 should be able to detect its surroundings so that it can avoid obstacles and reach its goal. It uses sensors such as digital cameras, infrared scans sonar and laser radar to detect the environment. It also makes use of an inertial sensors to monitor its speed, position and orientation. These sensors aid in navigation in a safe and secure manner and prevent collisions.
A range sensor is used to gauge the distance between the robot and the obstacle. The sensor can be positioned on the robot, in an automobile or on poles. It is important to keep in mind that the sensor could be affected by a myriad of factors such as wind, rain and fog. It is crucial to calibrate the sensors before each use.
A crucial step in obstacle detection is the identification of static obstacles, which can be accomplished using the results of the eight-neighbor-cell clustering algorithm. This method isn't particularly accurate because of the occlusion created by the distance between laser lines and the camera's angular speed. To overcome this problem, multi-frame fusion was used to increase the effectiveness of static obstacle detection.
The method of combining roadside unit-based and obstacle detection by a vehicle camera has been shown to improve the efficiency of processing data and reserve redundancy for subsequent navigation operations, such as path planning. This method produces a high-quality, reliable image of the surrounding. The method has been tested with other obstacle detection methods including YOLOv5 VIDAR, YOLOv5, and monocular ranging in outdoor comparison experiments.
The results of the experiment revealed that the algorithm was able accurately determine the height and location of an obstacle, as well as its tilt and rotation. It also showed a high performance in detecting the size of obstacles and its color. The method also demonstrated good stability and robustness even when faced with moving obstacles.