Ten Things Everyone Misunderstands About The Word "Lidar Robot Navigation" > 자유게시판

LiDAR Robot Navigation

LiDAR robot navigation is a complex combination of localization, mapping, and path planning. This article will introduce these concepts and explain how they function together with an easy example of the robot reaching a goal in a row of crop.

LiDAR sensors are low-power devices that prolong the life of batteries on a robot and reduce the amount of raw data needed for localization algorithms. This enables more iterations of the SLAM algorithm without overheating the GPU.

LiDAR Sensors

The sensor is the heart of a Lidar system. It releases laser pulses into the surrounding. These light pulses strike objects and bounce back to the sensor at various angles, based on the composition of the object. The sensor records the amount of time it takes for each return and then uses it to determine 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 according to their intended airborne or terrestrial application. Airborne lidars are usually connected to helicopters or an unmanned aerial vehicle (UAV). Terrestrial lidar robot vacuum is usually installed on a stationary robot platform.

To accurately measure distances, the sensor must be aware of the precise location of the robot at all times. This information is usually gathered using an array of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are utilized by LiDAR systems to calculate the exact position of the sensor within space and time. The information gathered is used to create a 3D representation of the surrounding.

LiDAR scanners are also able to identify different surface types, which is particularly useful for mapping environments with dense vegetation. When a pulse crosses a forest canopy it will usually register multiple returns. Usually, the first return is associated with the top of the trees while the last return is related to the ground surface. If the sensor can record each pulse as distinct, it is known as discrete return LiDAR.

Distinte return scanning can be helpful in studying the structure of surfaces. For instance, a forest area could yield a sequence of 1st, 2nd and 3rd return, with a final large pulse representing the ground. The ability to separate and store these returns as a point cloud permits detailed models of terrain.

Once an 3D model of the environment is built the robot will be able to use this data to navigate. This process involves localization and building a path that will reach a navigation "goal." It also involves dynamic obstacle detection. The latter is the method of identifying new obstacles that aren't present in the original map, 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 identify its location in relation to the map. Engineers use this information for a range of tasks, such as the planning of routes and obstacle detection.

To be able to use SLAM the robot needs to have a sensor that provides 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 will also require an inertial measurement unit (IMU) to provide basic positional information. The result is a system that can precisely track the position of your robot in an unspecified environment.

The SLAM process is extremely complex and many back-end solutions exist. Whatever solution you choose for your SLAM system, a successful SLAM system requires constant interaction between the range measurement device, the software that extracts the data, and the vehicle or robot. This is a dynamic process that is almost indestructible.

As the robot moves it adds scans to its map. The SLAM algorithm then compares these scans to previous ones using a process called scan matching. This allows loop closures to be identified. If a loop closure is identified it is then the SLAM algorithm uses this information to update its estimated robot trajectory.

Another factor that makes SLAM is the fact that the surrounding changes in time. For instance, if a robot is walking down an empty aisle at one point and then encounters stacks of pallets at the next location it will be unable to connecting these two points in its map. This is when handling dynamics becomes important, and this is a common characteristic of the modern Lidar SLAM algorithms.

Despite these difficulties, a properly configured SLAM system can be extremely effective for navigation and 3D scanning. It is particularly useful in environments where the robot isn't able to rely on GNSS for its positioning, such as an indoor factory floor. It is crucial to keep in mind that even a properly configured SLAM system may experience errors. It is essential to be able to spot these issues and comprehend how they impact the SLAM process to rectify them.

Mapping

The mapping function creates a map for a robot's environment. This includes the robot and its wheels, actuators, and everything else that is within its vision field. This map is used to aid in location, route planning, and obstacle detection. This is an area in which 3D Lidars are particularly useful as they can be treated as a 3D Camera (with one scanning plane).

Map building is a time-consuming process, but it pays off in the end. The ability to build a complete, coherent map of the surrounding area allows it to carry out high-precision navigation, as as navigate around obstacles.

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

There are many different mapping algorithms that can be utilized with LiDAR sensors. Cartographer is a popular algorithm that utilizes the two-phase pose graph optimization technique. It adjusts for drift while maintaining an unchanging global map. It is especially useful when paired with odometry.

Another option is GraphSLAM, which uses a system of linear equations to model the constraints of graph. The constraints are represented by an O matrix, and a vector X. Each vertice of the O matrix is an approximate distance from a landmark on X-vector. A GraphSLAM Update is a series additions and subtractions on these matrix elements. The result is that all O and X Vectors are updated in order to account for the new observations made by the robot.

Another useful mapping algorithm is SLAM+, which combines the use of odometry with mapping using an Extended Kalman filter (EKF). The EKF updates the uncertainty of the robot's position as well as the uncertainty of the features that were recorded by the sensor. The mapping function is able to make use of this information to better estimate its own location, allowing it to update the base map.

Obstacle Detection

A robot must be able to perceive its surroundings in order to avoid obstacles and reach its final point. It uses sensors such as digital cameras, infrared scans, sonar and laser radar to determine the surrounding. It also uses inertial sensor to measure its speed, location and its orientation. These sensors allow it to navigate without danger and avoid collisions.

A range sensor is used to determine the distance between an obstacle and Lidar robot Navigation a robot. 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 elements, including wind, rain and fog. Therefore, it is crucial to calibrate the sensor before every use.

The results of the eight neighbor cell clustering algorithm can be used to identify static obstacles. However this method is not very effective in detecting obstacles because of the occlusion caused by the distance between the different laser lines and the angle of the camera, which makes it difficult to detect static obstacles in one frame. To address this issue, multi-frame fusion was used to improve the accuracy of the static obstacle detection.

The method of combining roadside unit-based as well as vehicle camera obstacle detection has been proven to improve the data processing efficiency and reserve redundancy for further navigation operations, such as path planning. This method provides a high-quality, reliable image of the surrounding. The method has been compared with other obstacle detection techniques like YOLOv5 VIDAR, YOLOv5, and monocular ranging, in outdoor tests of comparison.

The results of the test revealed that the algorithm was able accurately determine the position and height of an obstacle, in addition to its rotation and tilt. It also showed a high performance in detecting the size of an obstacle and its color. The method was also robust and stable, even when obstacles were moving.