Lidar Robot Navigation 101: A Complete Guide For Beginners
LiDAR Robot Navigation
LiDAR robot navigation is a complex combination of localization, mapping, and path planning. This article will introduce these concepts and show how they function together with a simple example of the robot achieving a goal within the middle of a row of crops.
LiDAR sensors are low-power devices that prolong the life of batteries on robots and reduce the amount of raw data required for localization algorithms. This allows for a greater number of iterations of SLAM without overheating GPU.
LiDAR Sensors
The core of a lidar system is its sensor which emits laser light in the environment. These light pulses bounce off objects around them in different angles, based on their composition. The sensor records the time it takes for each return, which is then used to calculate distances. The sensor is typically mounted on a rotating platform, which allows it to scan the entire area at high speed (up to 10000 samples per second).
LiDAR sensors can be classified according to the type of sensor they're designed for, whether use in the air or on the ground. Airborne lidar systems are commonly mounted on aircrafts, helicopters, or unmanned aerial vehicles (UAVs). Terrestrial LiDAR systems are usually placed on a stationary robot platform.
To accurately measure distances, the sensor must know the exact position of the robot at all times. This information is gathered by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems utilize these sensors to compute the precise location of the sensor in space and time. This information is then used to build up an 3D map of the surrounding area.
LiDAR scanners can also detect different types of surfaces, which is particularly beneficial when mapping environments with dense vegetation. When a pulse passes a forest canopy, it will typically register multiple returns. Typically, the first return is attributed to the top of the trees, while the final return is attributed to the ground surface. If the sensor records these pulses separately and is referred to as discrete-return LiDAR.
Distinte return scanning can be useful for analyzing the structure of surfaces. For example forests can produce an array of 1st and 2nd returns, with the last one 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 map of the surrounding area has been created, the robot can begin to navigate based on this data. This process involves localization, constructing the path needed to reach a navigation 'goal,' and dynamic obstacle detection. This is the process of identifying obstacles that aren't present on the original map and then updating the plan in line with the new obstacles.
SLAM Algorithms
SLAM (simultaneous mapping and localization) is an algorithm which allows your robot to map its environment and then identify its location in relation to that map. Engineers make use of this information to perform a variety of purposes, including the planning of routes and obstacle detection.
To utilize SLAM, your robot needs to be equipped with a sensor that can provide range data (e.g. A computer that has the right software for processing the data, as well as either a camera or laser are required. You'll also require an IMU to provide basic information about your position. The system can determine the precise location of your robot in an unknown environment.
The SLAM process is a complex one and many back-end solutions are available. Whatever solution you select for the success of SLAM it requires a constant interaction between the range measurement device and the software that extracts the data, as well as the robot or vehicle. This is a highly dynamic process that has an almost infinite amount of variability.
When the robot moves, it adds scans to its map. The SLAM algorithm then compares these scans to the previous ones using a method known as scan matching. This allows loop closures to be established. The SLAM algorithm adjusts its estimated robot trajectory once loop closures are detected.
The fact that the environment can change over time is a further factor that complicates SLAM. For instance, if your robot is navigating an aisle that is empty at one point, and then encounters a stack of pallets at another point, it may have difficulty finding the two points on its map. The handling dynamics are crucial in this scenario and are a feature of many modern Lidar SLAM algorithm.
SLAM systems are extremely efficient at navigation and 3D scanning despite these limitations. It is especially useful in environments that don't allow the robot to rely on GNSS-based positioning, like an indoor factory floor. However, it's important to remember that even a properly configured SLAM system can be prone to errors. To fix these issues it is crucial to be able detect the effects of these errors and their implications on the SLAM process.
Mapping
The mapping function creates an image of the robot's surroundings which includes the robot as well as its wheels and actuators and everything else that is in the area of view. This map is used to perform localization, path planning and obstacle detection. This is a field in which 3D Lidars can be extremely useful, since they can be regarded as an 3D Camera (with only one scanning plane).
The map building process can take some time however, the end result pays off. The ability to build a complete and coherent map of the robot's surroundings allows it to move with high precision, as well as over obstacles.
As a rule, the higher the resolution of the sensor, the more precise will be the map. Not all robots require maps with high resolution. For instance floor sweepers might not require the same level detail as a robotic system for industrial use that is navigating factories of a large size.
For this reason, there are many different mapping algorithms to use with LiDAR sensors. Cartographer is a well-known algorithm that uses a two-phase pose graph optimization technique. It corrects for drift while maintaining a consistent global map. It is particularly effective when combined with Odometry.
GraphSLAM is a second option that uses a set linear equations to model the constraints in the form of a diagram. The constraints are represented by an O matrix, and a X-vector. Each vertice in the O matrix represents an approximate distance from the X-vector's landmark. A GraphSLAM Update is a sequence of additions and subtractions on these matrix elements. The end result is that all the O and X Vectors are updated in order to account for the new observations made by the robot.
SLAM+ is another useful mapping algorithm that combines odometry and mapping using an Extended Kalman filter (EKF). The EKF updates not only the uncertainty in the robot's current position but also the uncertainty in the features that have been drawn by the sensor. The mapping function will utilize this information to improve its own position, allowing it to update the underlying map.
Obstacle Detection
A robot needs to be able to see its surroundings to overcome obstacles and reach its destination. It uses sensors such as digital cameras, infrared scans, sonar, laser radar and others to sense the surroundings. It also uses inertial sensor to measure its speed, location and the direction. These sensors help it navigate in a safe way and prevent collisions.
One of the most important aspects of this process is obstacle detection, which involves the use of sensors to measure the distance between the robot and the obstacles. The sensor can be attached to the robot, a vehicle or even a pole. It is important to remember that the sensor could be affected by a variety of elements like rain, wind and fog. It is essential to calibrate the sensors prior every use.
www.robotvacuummops.com in obstacle detection is identifying static obstacles, which can be accomplished by using the results of the eight-neighbor cell clustering algorithm. This method isn't very accurate because of the occlusion induced by the distance between laser lines and the camera's angular velocity. To solve this issue, a technique of multi-frame fusion was developed to increase the accuracy of detection of static obstacles.
The technique of combining roadside camera-based obstacle detection with vehicle camera has proven to increase the efficiency of data processing. It also provides redundancy for other navigational tasks, like path planning. The result of this technique is a high-quality image of the surrounding environment that is more reliable than a single frame. In outdoor comparison tests the method was compared to other methods for detecting obstacles like YOLOv5 monocular ranging, VIDAR.
The results of the test revealed that the algorithm was able to accurately determine the height and location of an obstacle, as well as its tilt and rotation. It was also able identify the size and color of an object. The method was also robust and steady even when obstacles moved.