Five Lidar Robot Navigation Lessons From The Professionals
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작성자 Norberto Grullo… 작성일24-04-18 17:57 조회12회 댓글0건관련링크
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LiDAR Robot Navigation
LiDAR robots navigate using a combination of localization, mapping, and also path planning. This article will explain the concepts and show how they work by using a simple example where the robot reaches an objective within a row of plants.
LiDAR sensors are relatively low power requirements, which allows them to increase the life of a robot's battery and reduce the amount of raw data required for localization algorithms. This allows for more iterations of SLAM without overheating GPU.
LiDAR Sensors
The core of lidar systems is their sensor, which emits laser light pulses into the surrounding. These pulses bounce off surrounding objects at different angles based on their composition. The sensor measures how long it takes each pulse to return and uses that data to determine distances. The sensor is usually placed on a rotating platform allowing it to quickly scan the entire surrounding area at high speed (up to 10000 samples per second).
LiDAR sensors can be classified according to whether they're designed for use in the air or on the ground. Airborne lidar systems are usually mounted on aircrafts, helicopters or unmanned aerial vehicles (UAVs). Terrestrial vacuum lidar is usually mounted on a robotic platform that is stationary.
To accurately measure distances the sensor must be able to determine the exact location of the robot. This information is gathered using a combination of inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are employed by LiDAR systems to calculate the precise location of the sensor in space and time. The information gathered is used to build a 3D model of the environment.
LiDAR scanners are also able to detect different types of surface which is especially useful when mapping environments that have dense vegetation. For instance, if a pulse passes through a forest canopy it is likely to register multiple returns. The first one is typically attributed to the tops of the trees while the last is attributed with the surface of the ground. If the sensor records each pulse as distinct, it is known as discrete return LiDAR.
Discrete return scanning can also be helpful in studying surface structure. For instance, a forested region might 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 makes it possible for the creation of detailed terrain models.
Once a 3D model of the environment is created and the robot is capable of using this information to navigate. This involves localization, building a path to reach a goal for navigation,' and dynamic obstacle detection. This is the process that identifies new obstacles not included in the map that was created and adjusts the path plan according to the new obstacles.
SLAM Algorithms
SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its surroundings, and then determine its location in relation to the map. Engineers utilize this information for a range of tasks, including the planning of routes 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 to process the data as well as cameras or lasers are required. Also, you need an inertial measurement unit (IMU) to provide basic information about your position. The system will be able to track your robot's location accurately in a hazy environment.
The SLAM system is complex and there are many different back-end options. No matter which solution you select for the success of SLAM it requires a constant interaction between the range measurement device and the software that extracts data, as well as the vehicle or robot. This is a highly dynamic procedure that is prone to an unlimited amount of variation.
As the robot moves around, it adds new scans to its map. The SLAM algorithm compares these scans with prior ones using a process called scan matching. This aids in establishing loop closures. If a loop closure is identified when loop closure is detected, the SLAM algorithm utilizes this information to update its estimated robot trajectory.
Another factor that complicates SLAM is the fact that the scene changes in time. If, for example, your robot is walking down an aisle that is empty at one point, but then encounters a stack of pallets at a different location it may have trouble matching the two points on its map. This is when handling dynamics becomes critical, and this is a typical feature of modern Lidar SLAM algorithms.
Despite these issues, a properly-designed SLAM system is incredibly effective for navigation and 3D scanning. It is especially useful in environments that do not permit the robot to rely on GNSS positioning, such as an indoor factory floor. It is crucial to keep in mind that even a properly configured SLAM system can be prone to errors. To correct these mistakes it is crucial to be able to spot them and comprehend their impact on the SLAM process.
Mapping
The mapping function builds a map of the robot's surrounding, which includes the robot itself including its wheels and actuators, and everything else in the area of view. This map is used to perform the localization, planning of paths and obstacle detection. This is a domain where 3D Lidars are particularly useful because they can be regarded as an 3D Camera (with a single scanning plane).
The process of building maps may take a while however the results pay off. The ability to create an accurate, complete map of the surrounding area allows it to perform high-precision navigation as well as navigate around obstacles.
As a general rule of thumb, the greater resolution the sensor, more accurate the map will be. However there are exceptions to the requirement for high-resolution maps: for example floor sweepers might not need the same degree of detail as an industrial robot navigating factories with huge facilities.
There are a variety of mapping algorithms that can be employed with LiDAR sensors. One of the most well-known algorithms is Cartographer which employs a two-phase pose graph optimization technique to adjust for drift and keep a uniform global map. It is particularly useful when paired with the odometry information.
Another option is GraphSLAM, which uses a system of linear equations to model constraints of graph. The constraints are modelled as an O matrix and an X vector, with each vertex of the O matrix representing the distance to a landmark on the X vector. A GraphSLAM update consists of the addition and subtraction operations on these matrix elements, with the end result being that all of the O and lidar robot Navigation X vectors are updated to account for new information about the robot.
SLAM+ is another useful mapping algorithm that combines odometry with 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 mapped by the sensor. This information can be used by the mapping function to improve its own estimation of its location, and also to update the map.
Obstacle Detection
A robot must be able to perceive its surroundings to avoid obstacles and reach its final point. It employs sensors such as digital cameras, infrared scans, sonar, laser radar and others to detect the environment. Additionally, it utilizes inertial sensors to measure its speed, position and orientation. These sensors help it navigate in a safe manner and avoid collisions.
A key element of this process is obstacle detection that involves the use of sensors to measure the distance between the robot and obstacles. The sensor can be placed on the robot, inside an automobile or on poles. It is important to keep in mind that the sensor can be affected by many elements, including rain, wind, and fog. It is essential to calibrate the sensors prior every use.
The most important aspect of obstacle detection is the identification of static obstacles. This can be accomplished by using the results of the eight-neighbor cell clustering algorithm. This method is not very precise due to the occlusion caused by the distance between the laser lines and the camera's angular speed. To overcome this issue multi-frame fusion was employed to increase the accuracy of static obstacle detection.
The technique of combining roadside camera-based obstruction detection with the vehicle camera has shown to improve the efficiency of processing data. It also reserves redundancy for other navigation operations, like planning a path. This method creates a high-quality, reliable image of the environment. 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 showed that the algorithm was able to accurately identify the position and height of an obstacle, in addition to its rotation and tilt. It also had a good ability to determine the size of obstacles and its color. The method was also robust and reliable, even when obstacles were moving.
LiDAR robots navigate using a combination of localization, mapping, and also path planning. This article will explain the concepts and show how they work by using a simple example where the robot reaches an objective within a row of plants.LiDAR sensors are relatively low power requirements, which allows them to increase the life of a robot's battery and reduce the amount of raw data required for localization algorithms. This allows for more iterations of SLAM without overheating GPU.
LiDAR Sensors
The core of lidar systems is their sensor, which emits laser light pulses into the surrounding. These pulses bounce off surrounding objects at different angles based on their composition. The sensor measures how long it takes each pulse to return and uses that data to determine distances. The sensor is usually placed on a rotating platform allowing it to quickly scan the entire surrounding area at high speed (up to 10000 samples per second).
LiDAR sensors can be classified according to whether they're designed for use in the air or on the ground. Airborne lidar systems are usually mounted on aircrafts, helicopters or unmanned aerial vehicles (UAVs). Terrestrial vacuum lidar is usually mounted on a robotic platform that is stationary.
To accurately measure distances the sensor must be able to determine the exact location of the robot. This information is gathered using a combination of inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are employed by LiDAR systems to calculate the precise location of the sensor in space and time. The information gathered is used to build a 3D model of the environment.
LiDAR scanners are also able to detect different types of surface which is especially useful when mapping environments that have dense vegetation. For instance, if a pulse passes through a forest canopy it is likely to register multiple returns. The first one is typically attributed to the tops of the trees while the last is attributed with the surface of the ground. If the sensor records each pulse as distinct, it is known as discrete return LiDAR.
Discrete return scanning can also be helpful in studying surface structure. For instance, a forested region might 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 makes it possible for the creation of detailed terrain models.
Once a 3D model of the environment is created and the robot is capable of using this information to navigate. This involves localization, building a path to reach a goal for navigation,' and dynamic obstacle detection. This is the process that identifies new obstacles not included in the map that was created and adjusts the path plan according to the new obstacles.
SLAM Algorithms
SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its surroundings, and then determine its location in relation to the map. Engineers utilize this information for a range of tasks, including the planning of routes 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 to process the data as well as cameras or lasers are required. Also, you need an inertial measurement unit (IMU) to provide basic information about your position. The system will be able to track your robot's location accurately in a hazy environment.
The SLAM system is complex and there are many different back-end options. No matter which solution you select for the success of SLAM it requires a constant interaction between the range measurement device and the software that extracts data, as well as the vehicle or robot. This is a highly dynamic procedure that is prone to an unlimited amount of variation.
As the robot moves around, it adds new scans to its map. The SLAM algorithm compares these scans with prior ones using a process called scan matching. This aids in establishing loop closures. If a loop closure is identified when loop closure is detected, the SLAM algorithm utilizes this information to update its estimated robot trajectory.
Another factor that complicates SLAM is the fact that the scene changes in time. If, for example, your robot is walking down an aisle that is empty at one point, but then encounters a stack of pallets at a different location it may have trouble matching the two points on its map. This is when handling dynamics becomes critical, and this is a typical feature of modern Lidar SLAM algorithms.
Despite these issues, a properly-designed SLAM system is incredibly effective for navigation and 3D scanning. It is especially useful in environments that do not permit the robot to rely on GNSS positioning, such as an indoor factory floor. It is crucial to keep in mind that even a properly configured SLAM system can be prone to errors. To correct these mistakes it is crucial to be able to spot them and comprehend their impact on the SLAM process.
Mapping
The mapping function builds a map of the robot's surrounding, which includes the robot itself including its wheels and actuators, and everything else in the area of view. This map is used to perform the localization, planning of paths and obstacle detection. This is a domain where 3D Lidars are particularly useful because they can be regarded as an 3D Camera (with a single scanning plane).
The process of building maps may take a while however the results pay off. The ability to create an accurate, complete map of the surrounding area allows it to perform high-precision navigation as well as navigate around obstacles.
As a general rule of thumb, the greater resolution the sensor, more accurate the map will be. However there are exceptions to the requirement for high-resolution maps: for example floor sweepers might not need the same degree of detail as an industrial robot navigating factories with huge facilities.
There are a variety of mapping algorithms that can be employed with LiDAR sensors. One of the most well-known algorithms is Cartographer which employs a two-phase pose graph optimization technique to adjust for drift and keep a uniform global map. It is particularly useful when paired with the odometry information.
Another option is GraphSLAM, which uses a system of linear equations to model constraints of graph. The constraints are modelled as an O matrix and an X vector, with each vertex of the O matrix representing the distance to a landmark on the X vector. A GraphSLAM update consists of the addition and subtraction operations on these matrix elements, with the end result being that all of the O and lidar robot Navigation X vectors are updated to account for new information about the robot.
SLAM+ is another useful mapping algorithm that combines odometry with 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 mapped by the sensor. This information can be used by the mapping function to improve its own estimation of its location, and also to update the map.
Obstacle Detection
A robot must be able to perceive its surroundings to avoid obstacles and reach its final point. It employs sensors such as digital cameras, infrared scans, sonar, laser radar and others to detect the environment. Additionally, it utilizes inertial sensors to measure its speed, position and orientation. These sensors help it navigate in a safe manner and avoid collisions.
A key element of this process is obstacle detection that involves the use of sensors to measure the distance between the robot and obstacles. The sensor can be placed on the robot, inside an automobile or on poles. It is important to keep in mind that the sensor can be affected by many elements, including rain, wind, and fog. It is essential to calibrate the sensors prior every use.
The most important aspect of obstacle detection is the identification of static obstacles. This can be accomplished by using the results of the eight-neighbor cell clustering algorithm. This method is not very precise due to the occlusion caused by the distance between the laser lines and the camera's angular speed. To overcome this issue multi-frame fusion was employed to increase the accuracy of static obstacle detection.
The technique of combining roadside camera-based obstruction detection with the vehicle camera has shown to improve the efficiency of processing data. It also reserves redundancy for other navigation operations, like planning a path. This method creates a high-quality, reliable image of the environment. 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 showed that the algorithm was able to accurately identify the position and height of an obstacle, in addition to its rotation and tilt. It also had a good ability to determine the size of obstacles and its color. The method was also robust and reliable, even when obstacles were moving.
