• 목록
• 답변
• 글쓰기
• 게시판 리스트 옵션
  • 수정
  • 삭제

LiDAR Robot Navigation

LiDAR robot navigation is a complicated combination of localization, mapping and path planning. This article will explain these concepts and show how they function together with an example of a robot achieving its goal in the middle of a row of crops.

LiDAR sensors have modest power requirements, allowing them to increase the life of a robot's battery and reduce the raw data requirement for localization algorithms. This allows for more repetitions of SLAM without overheating the GPU.

LiDAR Sensors

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

LiDAR sensors can be classified based on the type of sensor they're designed for, whether airborne application or terrestrial application. Airborne lidars are usually attached to helicopters or unmanned aerial vehicles (UAV). Terrestrial LiDAR systems are generally mounted on a static best robot vacuum lidar platform.

To accurately measure distances the sensor must always know the exact location of the robot. This information is gathered by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are employed by LiDAR systems in order to determine the exact position of the sensor within space and time. This information is then used to create a 3D model of the environment.

LiDAR scanners can also be used to identify different surface types and types of surfaces, which is particularly useful when mapping environments that have dense vegetation. For instance, if a pulse passes through a forest canopy, it is common for it to register multiple returns. Typically, the first return is attributed to the top of the trees, while the last return is associated with the ground surface. If the sensor records each peak of these pulses as distinct, this what is lidar robot vacuum referred to as discrete return LiDAR.

The Discrete Return scans can be used to analyze the structure of surfaces. For instance, a forest region may yield a series of 1st and 2nd return pulses, with the final large pulse representing the ground. The ability to separate and record these returns as a point cloud allows for precise terrain models.

Once a 3D model of the environment is constructed, the robot will be capable of using this information to navigate. This involves localization as well as building a path that will get to a navigation "goal." It also involves dynamic obstacle detection. This 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 which allows your robot to map its environment and then determine its location relative to that map. Engineers make use of this information to perform a variety of tasks, including path planning and obstacle identification.

To be able to use SLAM the robot needs to have a sensor that gives range data (e.g. the laser or camera), and a computer that has the appropriate software to process the data. You'll also require an IMU to provide basic information about your position. The system will be able to track your robot's location accurately in an unknown environment.

The SLAM system is complex and there are a variety of back-end options. Whatever option you select for 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 highly dynamic procedure that is prone to an unlimited amount of variation.

As the robot moves it adds scans to its map. The SLAM algorithm then compares these scans with the previous ones using a method called scan matching. This aids in establishing loop closures. When a loop closure has been identified it is then the SLAM algorithm utilizes this information to update its estimate of the robot's trajectory.

Another issue that can hinder SLAM is the fact that the surrounding changes as time passes. For instance, if your robot walks through an empty aisle at one point, and then comes across pallets at the next location, it will have difficulty matching these two points in its map. Handling dynamics are important in this case, and they are a part of a lot of modern Lidar SLAM algorithm.

Despite these issues, a properly-designed SLAM system is incredibly effective for navigation and 3D scanning. It is especially useful in environments that don't allow the robot to rely on GNSS-based positioning, like an indoor factory floor. It is crucial to keep in mind that even a well-designed SLAM system could be affected by mistakes. It is vital to be able to spot these flaws and understand how they affect the SLAM process to rectify them.

Mapping

The mapping function builds a map of the robot's environment that includes the robot as well as its wheels and actuators, and everything else in the area of view. This map is used for localization, path planning and obstacle detection. This is a field in which 3D Lidars are especially helpful because they can be regarded as an 3D Camera (with one scanning plane).

The process of creating maps may take a while however, the end result pays off. The ability to create a complete and coherent map of a robot's environment allows it to navigate with high precision, and also over obstacles.

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

There are a variety of mapping algorithms that can be employed with LiDAR sensors. Cartographer is a well-known algorithm that uses a two-phase pose graph optimization technique. It corrects for drift while ensuring a consistent global map. It is particularly useful when combined with Odometry.

GraphSLAM is a different option, which uses a set of linear equations to model the constraints in the form of a diagram. The constraints are represented as an O matrix and a the X vector, with every vertice of the O matrix containing the distance to a point on the X vector. A GraphSLAM update is an array of additions and subtraction operations on these matrix elements which means that all of the X and O vectors are updated to account for new robot observations.

SLAM+ is another useful mapping algorithm that combines odometry with 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. This information can be utilized by the mapping function to improve its own estimation of its location and to update the map.

Obstacle Detection

A robot must be able to sense its surroundings in order to avoid obstacles and reach its goal point. It makes use of sensors like digital cameras, infrared scans sonar and laser radar to detect the environment. It also utilizes an inertial sensors to monitor its speed, location and orientation. These sensors allow it to navigate without danger and avoid collisions.

A range sensor is used to gauge the distance between an obstacle and a robot vacuum with object Avoidance Lidar. The sensor can be placed on the robot, inside a vehicle or on a pole. It is important to remember that the sensor can be affected by a variety of elements, including wind, rain and fog. It is essential to calibrate the sensors prior to every use.

The most important aspect of obstacle detection is the identification of static obstacles. This can be accomplished using the results of the eight-neighbor-cell clustering algorithm. However, this method has a low accuracy in detecting due to the occlusion created by the gap between the laser lines and the speed of the camera's angular velocity which makes it difficult to recognize static obstacles in one frame. To address this issue, a technique of multi-frame fusion was developed to increase the detection accuracy of static obstacles.

The method of combining roadside camera-based obstacle detection with vehicle camera has been proven to increase the efficiency of processing data. It also allows redundancy for other navigation operations such as planning a path. The result of this method is a high-quality image of the surrounding area that is more reliable than one frame. In outdoor comparison tests the method was compared with other methods for detecting obstacles such as YOLOv5 monocular ranging, and VIDAR.

The results of the test revealed that the algorithm was able accurately determine the height and location of an obstacle, in addition to its tilt and rotation. It also showed a high ability to determine the size of an obstacle and its color. The algorithm was also durable and stable even when obstacles moved.