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LiDAR Robot Navigation

LiDAR robot navigation is a complex combination of localization, mapping, and path planning. This article will explain the concepts and show how they work by using an example in which the robot achieves a goal within a row of plants.

LiDAR sensors are low-power devices that prolong the battery life of robots and decrease the amount of raw data required for localization algorithms. This allows for more versions of the SLAM algorithm without overheating the GPU.

LiDAR Sensors

The heart of a lidar based robot vacuum system is its sensor that emits pulsed laser light into the surrounding. These light pulses strike objects and bounce back to the sensor at a variety of angles, depending on the composition of the object. The sensor is able to measure the amount of time required to return each time, which is then used to determine distances. The sensor is typically mounted on a rotating platform, which allows it to scan the entire surrounding area at high speeds (up to 10000 samples per second).

LiDAR sensors are classified based on the type of sensor they are designed for applications on land or in the air. Airborne lidars are often connected to helicopters or an unmanned aerial vehicle (UAV). Terrestrial LiDAR systems are generally mounted on a stationary robot platform.

To accurately measure distances, the sensor needs to know the exact position of the robot at all times. This information is usually gathered through a combination of inertial measurement units (IMUs), GPS, and time-keeping electronics. lidar navigation robot vacuum systems use sensors to compute the exact location of the sensor in space and time, which is then used to build up a 3D map of the environment.

LiDAR scanners can also identify various types of surfaces which is especially useful when mapping environments that have dense vegetation. For instance, when an incoming pulse is reflected through a forest canopy it will typically register several returns. Usually, the first return is attributed to the top of the trees and the last one is associated with the ground surface. If the sensor captures these pulses separately this is known as discrete-return LiDAR.

Discrete return scanning can also be helpful in analysing surface structure. For example the forest may result in 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 a 3D model of the surrounding area has been built and the robot is able to navigate using this information. This process involves localization and making a path that will take it to a specific navigation "goal." It also involves dynamic obstacle detection. This is the method of identifying new obstacles that aren't present in the map originally, 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 relative to that map. Engineers make use of this information to perform a variety of tasks, such as planning routes and obstacle detection.

For SLAM to function the robot needs an instrument (e.g. A computer that has the right software for processing the data and either a camera or laser are required. Also, you will require an IMU to provide basic positioning information. The result is a system that can precisely track the position of your robot in an unknown environment.

The SLAM system is complicated and offers a myriad of back-end options. Whatever option you choose to implement a successful SLAM, it requires constant communication between the range measurement device and the software that collects data and the vehicle or robot. This is a dynamic process with a virtually unlimited variability.

As the robot moves about the area, it adds new scans to its map. The SLAM algorithm then compares these scans with the previous ones using a method known as scan matching. This allows loop closures to be established. The SLAM algorithm is updated with its estimated robot trajectory once the loop has been closed discovered.

The fact that the surrounding can change in time is another issue that makes it more difficult for SLAM. For instance, if your robot is walking down an empty aisle at one point, and then comes across pallets at the next point, it will have difficulty finding these two points on its map. Dynamic handling is crucial in this situation, and they are a part of a lot of modern lidar product SLAM algorithm.

Despite these challenges however, a properly designed SLAM system is extremely efficient for navigation and 3D scanning. It is particularly beneficial in situations that don't rely on GNSS for its positioning for example, an indoor factory floor. However, it is important to note that even a well-configured SLAM system can be prone to errors. To fix these issues, it is important to be able to recognize the effects of these errors and their implications on the SLAM process.

Mapping

The mapping function builds a map of the robot's surroundings, which includes the robot itself, its wheels and actuators as well as everything else within the area of view. This map is used for the localization, planning of paths and obstacle detection. This is a field where 3D Lidars can be extremely useful because they can be regarded as a 3D Camera (with one scanning plane).

Map creation is a time-consuming process however, it is worth it in the end. The ability to create an accurate, complete map of the robot's environment allows it to perform high-precision navigation as well being able to navigate around obstacles.

As a rule, the higher the resolution of the sensor, then the more precise will be the map. Not all robots require high-resolution maps. For example, a floor sweeping robot may not require the same level of detail as an industrial robotic system that is navigating factories of a large size.

To this end, there are a number of different mapping algorithms that can be used with LiDAR sensors. Cartographer is a well-known algorithm that employs the two-phase pose graph optimization technique. It corrects for drift while ensuring a consistent global map. It is especially beneficial when used in conjunction with the odometry information.

Another option is GraphSLAM that employs a system of linear equations to represent the constraints in a graph. The constraints are modeled as an O matrix and a one-dimensional X vector, each vertex of the O matrix representing a distance to a landmark on the X vector. A GraphSLAM update is the addition and subtraction operations on these matrix elements, and the result is that all of the O and X vectors are updated to reflect new observations of the robot.

Another useful 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 recorded 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 must be able to perceive its surroundings to avoid obstacles and reach its goal point. It employs sensors such as digital cameras, infrared scans, sonar, laser radar and others to determine the surrounding. Additionally, it utilizes inertial sensors that measure its speed and position, as well as its orientation. These sensors assist it in navigating in a safe and secure manner and prevent collisions.

One of the most important aspects of this process is the detection of obstacles that consists of the use of a range sensor to determine the distance between the robot and obstacles. The sensor can be placed on the robot, inside the vehicle, or on poles. It is important to remember that the sensor can be affected by various factors, such as rain, wind, and fog. Therefore, it is essential to calibrate the sensor before every 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 accuracy in detecting because of the occlusion caused by the gap between the laser lines and the speed of the camera's angular velocity, which makes it difficult to detect static obstacles in a single frame. To address this issue multi-frame fusion was employed to improve the accuracy of static obstacle detection.

The method of combining roadside unit-based and vehicle camera obstacle detection has been shown to improve the data processing efficiency and reserve redundancy for future navigation operations, such as path planning. The result of this method is a high-quality image of the surrounding environment that is more reliable than a single frame. 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 height and location of an obstacle, as well as its tilt and rotation. It also had a good performance in detecting the size of an obstacle and its color. The method also demonstrated good stability and robustness even in the presence of moving obstacles.