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LiDAR Robot Navigation

LiDAR robot navigation is a complicated combination of localization, mapping and path planning. This article will present these concepts and demonstrate how they work together using a simple example of the robot achieving a goal within a row of crops.

LiDAR sensors are low-power devices that can prolong the life of batteries on robots and decrease the amount of raw data required for localization algorithms. This allows for a greater number of iterations of SLAM without overheating the GPU.

LiDAR Sensors

The core of a lidar system is its sensor that emits pulsed laser light into the surrounding. These light pulses bounce off the surrounding objects at different angles based on their composition. The sensor monitors the time it takes for each pulse to return, and uses that data to calculate distances. The sensor is typically mounted on a rotating platform, allowing it to quickly scan the entire surrounding area at high speeds (up to 10000 samples per second).

LiDAR sensors are classified by 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 is usually installed on a robotic platform that is stationary.

To accurately measure distances, the sensor must always know the exact location of the robot. This information is usually gathered using a combination of inertial measuring units (IMUs), GPS, and time-keeping electronics. These sensors are used by LiDAR systems to calculate the exact location of the sensor within the space and time. This information is then used to create a 3D model of the environment.

LiDAR scanners can also identify different kinds of surfaces, which is especially beneficial when mapping environments with dense vegetation. For instance, when an incoming pulse is reflected through a canopy of trees, it is common for it to register multiple returns. The first one is typically associated with the tops of the trees while the second one is attributed to the surface of the ground. If the sensor captures these pulses separately and is referred to as discrete-return LiDAR.

Discrete return scanning can also be useful for analyzing the structure of surfaces. For instance, a forest region might yield an array of 1st, 2nd and 3rd return, with a final large pulse representing the ground. The ability to separate and record these returns as a point cloud allows for detailed models of terrain.

Once an 3D map of the surrounding area is created, the robot can begin to navigate using this data. This involves localization and creating a path to take it to a specific navigation "goal." It also involves dynamic obstacle detection. This is the method of identifying new obstacles that are not present in the map originally, and adjusting the path plan accordingly.

SLAM Algorithms

SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to create an image of its surroundings and then determine the position of the robot relative to the map. Engineers use the information to perform a variety of tasks, such as path planning and obstacle identification.

To allow SLAM to function the robot needs sensors (e.g. A computer with the appropriate software for processing the data, as well as cameras or lasers are required. You will also need an IMU to provide basic information about your position. The result is a system that can precisely track the position of your robot in an unspecified environment.

The SLAM system is complicated and there are many different back-end options. Regardless of which solution you choose for your SLAM system, a successful SLAM system requires a constant interaction between the range measurement device, the software that extracts the data and the vehicle or vacuum robot with lidar. This is a highly dynamic process that is prone to an unlimited amount of variation.

As the robot moves and around, it adds new scans to its map. The SLAM algorithm analyzes these scans against previous ones by using a process known as scan matching. This allows loop closures to be established. When a loop closure is detected when loop closure what is lidar navigation robot vacuum detected, the SLAM algorithm utilizes this information to update its estimated robot trajectory.

Another issue that can hinder SLAM is the fact that the environment changes over time. If, for instance, your robot is navigating an aisle that is empty at one point, and then comes across a pile of pallets at a different location it may have trouble connecting the two points on its map. This is where handling dynamics becomes critical, and this is a typical characteristic of the modern Lidar SLAM algorithms.

Despite these difficulties, a properly-designed SLAM system is extremely efficient for navigation and 3D scanning. It is especially beneficial in situations where the robot can't rely on GNSS for positioning for positioning, like an indoor factory floor. It is important to remember that even a properly configured SLAM system can experience mistakes. To correct these errors it is essential to be able detect them and comprehend their impact on the SLAM process.

Mapping

The mapping function creates a map of the robot's surroundings. This includes the robot, its wheels, actuators and everything else within its vision field. The map is used for localization, route planning and obstacle detection. This is an area where 3D Lidars can be extremely useful as they can be treated as a 3D Camera (with a single scanning plane).

Map creation can be a lengthy process however, it is worth it in the end. The ability to build a complete and consistent map of a robot's environment allows it to move with high precision, and also over obstacles.

The higher the resolution of the sensor then the more accurate will be the map. However it is not necessary for all robots to have high-resolution maps. For example floor sweepers may not require the same amount of detail as an industrial robot that is navigating large factory facilities.

There are a variety of mapping algorithms that can be utilized with lidar vacuum robot sensors. Cartographer is a very popular algorithm that uses a two-phase pose graph optimization technique. It corrects for drift while maintaining an unchanging global map. It is particularly beneficial when used in conjunction with odometry data.

GraphSLAM is a second option which utilizes a set of linear equations to represent the constraints in the form of a diagram. The constraints are represented as an O matrix, and an vector X. Each vertice of the O matrix is the distance to a landmark on X-vector. A GraphSLAM Update is a series subtractions and additions to these matrix elements. The end result is that both the O and X Vectors are updated to account for the new observations made by the robot.

Another efficient mapping algorithm is SLAM+, which 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 were drawn by the sensor. The mapping function can then utilize this information to improve its own location, allowing it to update the base map.

Obstacle Detection

A robot needs to be able to see its surroundings to avoid obstacles and get to its desired point. It utilizes sensors such as digital cameras, infrared scanners, sonar and laser radar to sense its surroundings. It also utilizes an inertial sensor to measure its speed, position and orientation. These sensors help it navigate in a safe way and prevent collisions.

A key element of this process is obstacle detection that consists of the use of a range sensor to determine the distance between the robot and obstacles. The sensor can be positioned on the robot, inside the vehicle, or on the pole. It is crucial to keep in mind that the sensor could be affected by various elements, including rain, wind, and fog. Therefore, it is crucial to calibrate the sensor prior every use.

The results of the eight neighbor cell clustering algorithm can be used to identify static obstacles. However this method has a low detection accuracy because of the occlusion caused by the gap between the laser lines and the angular velocity of the camera, which makes it difficult to identify static obstacles in one frame. To address this issue, a method of multi-frame fusion was developed to improve the detection accuracy of static obstacles.

The method of combining roadside unit-based and vehicle camera obstacle detection has been shown to improve the efficiency of data processing and reserve redundancy for future navigational operations, like path planning. The result of this method is a high-quality picture 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, and VIDAR.

The results of the test proved that the algorithm could correctly identify the height and location of obstacles as well as its tilt and rotation. It was also able detect the color and size of an object. The method was also reliable and stable even when obstacles moved.