Why The Lidar Robot Navigation Is Beneficial When COVID-19 Is In Session
LiDAR Robot Navigation
LiDAR robots navigate using a combination of localization and mapping, and also path planning. This article will outline the concepts and demonstrate how they work using an example in which the robot is able to reach the desired goal within the space of a row of plants.
LiDAR sensors are relatively low power requirements, allowing them to extend a robot's battery life 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 sensor is the core of the Lidar system. It emits laser pulses into the environment. These pulses hit surrounding objects and bounce back to the sensor at various angles, depending on the structure of the object. The sensor measures how long it takes for each pulse to return and uses that data to calculate distances. Sensors are positioned on rotating platforms, which allows them to scan the surrounding area quickly and at high speeds (10000 samples per second).
LiDAR sensors are classified according to their intended applications in the air or on land. Airborne lidars are usually attached to helicopters or UAVs, which are unmanned. (UAV). Terrestrial LiDAR is usually mounted 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 gathered using a combination of inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems utilize these sensors to compute the exact location of the sensor in space and time. This information is then used to create an 3D map of the surroundings.
LiDAR scanners can also identify different kinds of surfaces, which is particularly useful when mapping environments that have dense vegetation. When a pulse crosses a forest canopy, it is likely to generate multiple returns. The first return is associated with the top of the trees while the final return is attributed to the ground surface. If the sensor captures each pulse as distinct, it is called discrete return LiDAR.
The use of Discrete Return scanning can be helpful in studying the structure of surfaces. For instance, a forested area could yield a sequence of 1st, 2nd, and 3rd returns, with a final, large pulse representing the bare ground. The ability to divide these returns and save them as a point cloud allows for the creation of precise terrain models.
Once a 3D model of the environment is created and the robot is capable of using this information to navigate. This process involves localization, constructing the path needed to get to a destination and dynamic obstacle detection. This process detects new obstacles that are not listed in the map that was created and adjusts the path plan according to the new obstacles.
SLAM Algorithms
SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to construct an outline of its surroundings and then determine where it is relative to the map. Engineers utilize this information for a variety of tasks, such as path planning and obstacle detection.
To be able to use SLAM, your robot needs to have a sensor that provides range data (e.g. A computer with the appropriate software for processing the data and a camera or a laser are required. You also need an inertial measurement unit (IMU) to provide basic information about your position. The result is a system that can precisely track the position of your robot in a hazy environment.
The SLAM system is complex and there are a variety of back-end options. No matter which solution you select for a successful SLAM it requires constant interaction between the range measurement device and the software that extracts data and also the vehicle or robot. It is a dynamic process with almost infinite variability.
As the robot moves, it adds scans to its map. The SLAM algorithm then compares these scans with previous ones using a process known as scan matching. This helps to establish loop closures. If a loop closure is discovered it is then the SLAM algorithm utilizes this information to update its estimate of the robot's trajectory.
The fact that the surrounding changes in time is another issue 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 a difficult time finding these two points on its map. This is where the handling of dynamics becomes crucial and is a common feature of the modern Lidar SLAM algorithms.
SLAM systems are extremely effective at navigation and 3D scanning despite the challenges. It is particularly beneficial in environments that don't allow the robot to rely on GNSS-based position, such as an indoor factory floor. However, it's important to keep in mind that even a properly configured SLAM system can be prone to errors. It is crucial to be able recognize these flaws and understand how they affect the SLAM process to fix them.
Mapping
The mapping function creates a map of the robot's surroundings. This includes the robot and its wheels, actuators, and everything else within its vision field. The map is used for the localization of the robot, route planning and obstacle detection. This is an area in which 3D lidars can be extremely useful, as they can be utilized as a 3D camera (with one scan plane).
Map creation is a time-consuming process but it pays off in the end. The ability to create an accurate, complete map of the robot's surroundings allows it to perform high-precision navigation as well being able to navigate around obstacles.
The greater the resolution of the sensor, the more precise will be the map. Not all robots require high-resolution maps. For instance floor sweepers might not require the same level detail as a robotic system for industrial use navigating large factories.
There are a variety of mapping algorithms that can be employed with LiDAR sensors. One popular algorithm is called Cartographer which utilizes a two-phase pose graph optimization technique to correct for drift and maintain a consistent global map. It is particularly beneficial when used in conjunction with Odometry data.
GraphSLAM is a different option, which uses a set of linear equations to represent constraints in diagrams. The constraints are modeled as an O matrix and an X vector, with each vertex of the O matrix containing the distance to a point on the X vector. A GraphSLAM update consists of 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 account for new information about the robot.
Another helpful mapping algorithm is SLAM+, which combines odometry and mapping using an Extended Kalman Filter (EKF). The EKF changes the uncertainty of the robot's location as well as the uncertainty of the features that were recorded by the sensor. This information can be utilized 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 perceive its environment to overcome obstacles and reach its destination. It uses sensors like digital cameras, infrared scanners, sonar and laser radar to determine its surroundings. Additionally, it utilizes inertial sensors to measure its speed, position and orientation. These sensors aid in navigation in a safe way and avoid collisions.
A key element of this process is obstacle detection that involves the use of an IR range sensor to measure the distance between the robot and the obstacles. The sensor can be attached to the robot, a vehicle or a pole. It is important to keep in mind that the sensor can be affected by a variety of factors, such as wind, rain, and fog. It is important to calibrate the sensors prior to 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. However, this method has a low detection accuracy because of the occlusion caused by the distance between the different laser lines and the angle of the camera making it difficult to identify static obstacles in one frame. To overcome this issue multi-frame fusion was implemented to improve the accuracy of the static obstacle detection.
best robot vacuum with lidar of combining roadside unit-based as well as obstacle detection by a vehicle camera has been proven to improve the efficiency of processing data and reserve redundancy for further navigational tasks, like path planning. The result of this technique is a high-quality image of the surrounding environment that is more reliable than one frame. In outdoor comparison experiments the method was compared against other methods for detecting obstacles like YOLOv5 monocular ranging, VIDAR.
The results of the test proved that the algorithm was able to correctly identify the position and height of an obstacle, as well as its tilt and rotation. It also had a good performance in detecting the size of the obstacle and its color. The algorithm was also durable and stable, even when obstacles were moving.