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LiDAR Robot Navigation
LiDAR robot navigation is a complicated combination of mapping, localization and path planning. This article will introduce these concepts and demonstrate how they work together using an example of a robot vacuums with obstacle avoidance lidar achieving a goal within a row of crop.
LiDAR sensors are relatively low power requirements, which allows them to increase the battery life of a robot and decrease the raw data requirement for localization algorithms. This allows for more repetitions of SLAM without overheating the GPU.
LiDAR Sensors
The sensor is the core of the Lidar system. It releases laser pulses into the environment. The light waves bounce off objects around them at different angles depending on their composition. The sensor monitors the time it takes each pulse to return, and utilizes that information to calculate distances. The sensor is typically mounted on a rotating platform which allows it to scan the entire area at high speeds (up to 10000 samples per second).
LiDAR sensors are classified based on whether they're intended for applications in the air or on land. Airborne lidars are usually connected to helicopters or an unmanned aerial vehicle (UAV). Terrestrial lidar vacuum systems are usually placed on a stationary robot platform.
To accurately measure distances, the sensor must always know the exact location of the robot. This information is captured by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems utilize 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 surroundings.
LiDAR scanners can also be used to identify different surface types which is especially beneficial for mapping environments with dense vegetation. When a pulse passes a forest canopy, it is likely to generate multiple returns. The first return is usually associated with the tops of the trees, while the last is attributed with the surface of the ground. If the sensor captures each pulse as distinct, this is known as discrete return lidar navigation.
The Discrete Return scans can be used to determine surface structure. For instance, a forest region may yield a series of 1st and 2nd returns vacuum with lidar the final large pulse representing bare ground. The ability to separate and store these returns in a point-cloud permits detailed terrain models.
Once a 3D map of the surrounding area has been built and the robot has begun to navigate using this information. This process involves localization, creating an appropriate path to get to a destination,' and dynamic obstacle detection. This process identifies new obstacles not included in the map that was created and then updates the plan of travel according to the new obstacles.
SLAM Algorithms
SLAM (simultaneous mapping and localization) is an algorithm which allows your robot to map its surroundings and then determine its position relative to that map. Engineers make use of this information for a range of tasks, such as the planning of routes and obstacle detection.
To allow SLAM to work, your robot must have sensors (e.g. laser or camera), and a computer running the appropriate software to process the data. Also, you need an inertial measurement unit (IMU) to provide basic information on your location. The system can track your robot's location accurately in a hazy environment.
The SLAM system is complicated and there are many different back-end options. Whatever solution you choose for an effective SLAM, it requires constant interaction between the range measurement device and the software that collects data and also the robot vacuum with lidar and camera or vehicle. This is a dynamic process that is almost indestructible.
As the robot moves it adds scans to its map. The SLAM algorithm compares these scans to previous ones by making use of a process known as scan matching. This allows loop closures to be established. If a loop closure is identified it is then the SLAM algorithm makes use of this information to update its estimate of the robot's trajectory.
Another issue that can hinder SLAM is the fact that the environment changes over time. If, for instance, your robot is walking down an aisle that is empty at one point, but then comes across a pile of pallets at a different location, it may have difficulty connecting the two points on its map. This is where handling dynamics becomes critical and is a typical feature of the modern Lidar SLAM algorithms.
Despite these difficulties, a properly configured SLAM system is extremely efficient for navigation and 3D scanning. It is particularly beneficial in environments that don't permit the robot to rely on GNSS position, such as an indoor factory floor. It's important to remember that even a properly configured SLAM system could be affected by errors. To correct these errors, it is important to be able to recognize them and understand their impact on the SLAM process.
Mapping
The mapping function builds an outline of the robot's environment that includes the robot, its wheels and actuators, and everything else in the area of view. The map is used for the localization of the robot, route planning and obstacle detection. This is an area in which 3D lidars can be extremely useful, as they can be utilized as the equivalent of a 3D camera (with one scan plane).
Map building is a long-winded process however, it is worth it in the end. The ability to create a complete and coherent map of the robot's surroundings allows it to navigate with great precision, and also over obstacles.
As a rule of thumb, the greater 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 of detail as a robotic system for industrial use that is navigating factories of a large size.
This is why there are many different mapping algorithms that can be used with LiDAR sensors. One popular algorithm is called Cartographer which utilizes two-phase pose graph optimization technique to adjust for drift and keep a consistent global map. It is especially useful when paired with Odometry data.
GraphSLAM is a different option, which utilizes a set of linear equations to model the constraints in diagrams. The constraints are represented as an O matrix, and an X-vector. Each vertice of the O matrix contains the distance to an X-vector landmark. A GraphSLAM update consists of an array of additions and subtraction operations on these matrix elements, and the result is that all of the X and O vectors are updated to account for new robot observations.
Another helpful mapping algorithm is SLAM+, which combines odometry and mapping using an Extended Kalman filter (EKF). The EKF alters the uncertainty of the robot's position as well as the uncertainty of the features recorded by the sensor. The mapping function is able to utilize this information to improve its own position, which allows it to update the base map.
Obstacle Detection
A robot must be able to perceive its surroundings so it can avoid obstacles and get to its desired point. It uses sensors such as digital cameras, infrared scans, laser radar, and sonar to detect the environment. It also makes use of an inertial sensor to measure its speed, position and orientation. These sensors enable it to navigate without danger and avoid collisions.
One of the most important aspects of this process is the detection of obstacles, which involves the use of sensors to measure the distance between the robot and the obstacles. The sensor can be positioned on the robot, inside the vehicle, or on a pole. It is important to keep in mind that the sensor can be affected by various elements, including rain, wind, and fog. It is important to calibrate the sensors before each use.
The results of the eight neighbor cell clustering algorithm can be used to determine static obstacles. This method is not very accurate because of the occlusion induced by the distance between laser lines and the camera's angular speed. To overcome this issue multi-frame fusion was implemented to improve the accuracy of static obstacle detection.
The method of combining roadside unit-based as well as obstacle detection by a vehicle camera has been shown to improve the efficiency of processing data and reserve redundancy for subsequent navigational operations, like path planning. This method provides a high-quality, reliable image of the environment. The method has been tested with other obstacle detection methods like YOLOv5 VIDAR, YOLOv5, and monocular ranging, in outdoor tests of comparison.
The results of the test proved that the algorithm was able correctly identify the position and height of an obstacle, in addition to its rotation and tilt. It also had a good performance in detecting the size of an obstacle and its color. The method also showed excellent stability and durability even when faced with moving obstacles.
LiDAR robot navigation is a complicated combination of mapping, localization and path planning. This article will introduce these concepts and demonstrate how they work together using an example of a robot vacuums with obstacle avoidance lidar achieving a goal within a row of crop.
LiDAR sensors are relatively low power requirements, which allows them to increase the battery life of a robot and decrease the raw data requirement for localization algorithms. This allows for more repetitions of SLAM without overheating the GPU.
LiDAR Sensors
The sensor is the core of the Lidar system. It releases laser pulses into the environment. The light waves bounce off objects around them at different angles depending on their composition. The sensor monitors the time it takes each pulse to return, and utilizes that information to calculate distances. The sensor is typically mounted on a rotating platform which allows it to scan the entire area at high speeds (up to 10000 samples per second).
LiDAR sensors are classified based on whether they're intended for applications in the air or on land. Airborne lidars are usually connected to helicopters or an unmanned aerial vehicle (UAV). Terrestrial lidar vacuum systems are usually placed on a stationary robot platform.
To accurately measure distances, the sensor must always know the exact location of the robot. This information is captured by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems utilize 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 surroundings.
LiDAR scanners can also be used to identify different surface types which is especially beneficial for mapping environments with dense vegetation. When a pulse passes a forest canopy, it is likely to generate multiple returns. The first return is usually associated with the tops of the trees, while the last is attributed with the surface of the ground. If the sensor captures each pulse as distinct, this is known as discrete return lidar navigation.
The Discrete Return scans can be used to determine surface structure. For instance, a forest region may yield a series of 1st and 2nd returns vacuum with lidar the final large pulse representing bare ground. The ability to separate and store these returns in a point-cloud permits detailed terrain models.
Once a 3D map of the surrounding area has been built and the robot has begun to navigate using this information. This process involves localization, creating an appropriate path to get to a destination,' and dynamic obstacle detection. This process identifies new obstacles not included in the map that was created and then updates the plan of travel according to the new obstacles.
SLAM Algorithms
SLAM (simultaneous mapping and localization) is an algorithm which allows your robot to map its surroundings and then determine its position relative to that map. Engineers make use of this information for a range of tasks, such as the planning of routes and obstacle detection.
To allow SLAM to work, your robot must have sensors (e.g. laser or camera), and a computer running the appropriate software to process the data. Also, you need an inertial measurement unit (IMU) to provide basic information on your location. The system can track your robot's location accurately in a hazy environment.
The SLAM system is complicated and there are many different back-end options. Whatever solution you choose for an effective SLAM, it requires constant interaction between the range measurement device and the software that collects data and also the robot vacuum with lidar and camera or vehicle. This is a dynamic process that is almost indestructible.
As the robot moves it adds scans to its map. The SLAM algorithm compares these scans to previous ones by making use of a process known as scan matching. This allows loop closures to be established. If a loop closure is identified it is then the SLAM algorithm makes use of this information to update its estimate of the robot's trajectory.
Another issue that can hinder SLAM is the fact that the environment changes over time. If, for instance, your robot is walking down an aisle that is empty at one point, but then comes across a pile of pallets at a different location, it may have difficulty connecting the two points on its map. This is where handling dynamics becomes critical and is a typical feature of the modern Lidar SLAM algorithms.
Despite these difficulties, a properly configured SLAM system is extremely efficient for navigation and 3D scanning. It is particularly beneficial in environments that don't permit the robot to rely on GNSS position, such as an indoor factory floor. It's important to remember that even a properly configured SLAM system could be affected by errors. To correct these errors, it is important to be able to recognize them and understand their impact on the SLAM process.
Mapping
The mapping function builds an outline of the robot's environment that includes the robot, its wheels and actuators, and everything else in the area of view. The map is used for the localization of the robot, route planning and obstacle detection. This is an area in which 3D lidars can be extremely useful, as they can be utilized as the equivalent of a 3D camera (with one scan plane).
Map building is a long-winded process however, it is worth it in the end. The ability to create a complete and coherent map of the robot's surroundings allows it to navigate with great precision, and also over obstacles.
As a rule of thumb, the greater 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 of detail as a robotic system for industrial use that is navigating factories of a large size.
This is why there are many different mapping algorithms that can be used with LiDAR sensors. One popular algorithm is called Cartographer which utilizes two-phase pose graph optimization technique to adjust for drift and keep a consistent global map. It is especially useful when paired with Odometry data.
GraphSLAM is a different option, which utilizes a set of linear equations to model the constraints in diagrams. The constraints are represented as an O matrix, and an X-vector. Each vertice of the O matrix contains the distance to an X-vector landmark. A GraphSLAM update consists of an array of additions and subtraction operations on these matrix elements, and the result is that all of the X and O vectors are updated to account for new robot observations.
Another helpful mapping algorithm is SLAM+, which combines odometry and mapping using an Extended Kalman filter (EKF). The EKF alters the uncertainty of the robot's position as well as the uncertainty of the features recorded by the sensor. The mapping function is able to utilize this information to improve its own position, which allows it to update the base map.
Obstacle Detection
A robot must be able to perceive its surroundings so it can avoid obstacles and get to its desired point. It uses sensors such as digital cameras, infrared scans, laser radar, and sonar to detect the environment. It also makes use of an inertial sensor to measure its speed, position and orientation. These sensors enable it to navigate without danger and avoid collisions.
One of the most important aspects of this process is the detection of obstacles, which involves the use of sensors to measure the distance between the robot and the obstacles. The sensor can be positioned on the robot, inside the vehicle, or on a pole. It is important to keep in mind that the sensor can be affected by various elements, including rain, wind, and fog. It is important to calibrate the sensors before each use.
The results of the eight neighbor cell clustering algorithm can be used to determine static obstacles. This method is not very accurate because of the occlusion induced by the distance between laser lines and the camera's angular speed. To overcome this issue multi-frame fusion was implemented to improve the accuracy of static obstacle detection.
The method of combining roadside unit-based as well as obstacle detection by a vehicle camera has been shown to improve the efficiency of processing data and reserve redundancy for subsequent navigational operations, like path planning. This method provides a high-quality, reliable image of the environment. The method has been tested with other obstacle detection methods like YOLOv5 VIDAR, YOLOv5, and monocular ranging, in outdoor tests of comparison.
The results of the test proved that the algorithm was able correctly identify the position and height of an obstacle, in addition to its rotation and tilt. It also had a good performance in detecting the size of an obstacle and its color. The method also showed excellent stability and durability even when faced with moving obstacles.