Shari Dinkins asked 1 month ago
LiDAR Robot Navigation
LiDAR robot navigation is a sophisticated combination of localization, mapping, and path planning. This article will introduce these concepts and show how they work together using an example of a robot achieving a goal within a row of crops.
LiDAR sensors are low-power devices which can prolong the battery life of robots and decrease the amount of raw data needed for localization algorithms. This allows for more iterations of SLAM without overheating GPU.
LiDAR Sensors
The sensor is the heart of a Lidar system. It releases laser pulses into the surrounding. The light waves hit objects around and bounce back to the sensor at a variety of angles, based on the structure of the object. The sensor is able to measure the amount of time it takes for each return and uses this 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 can be classified according to the type of sensor they’re designed for, whether airborne application or terrestrial application. Airborne lidars are often attached to helicopters or unmanned aerial vehicles (UAV). Terrestrial LiDAR is usually mounted on a robot platform that is stationary.
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 to calculate the precise location of the sensor within space and time. This information is then used to create a 3D model of the surrounding.
LiDAR scanners can also identify different types of surfaces, which is particularly useful when mapping environments that have dense vegetation. When a pulse passes through a forest canopy it will usually register multiple returns. The first return is usually attributable to the tops of the trees, while the second is associated with the surface of the ground. If the sensor records these pulses in a separate way this is known as discrete-return lidar robot vacuum.
Discrete return scanning can also be useful in studying the structure of surfaces. For instance, a forested region could produce a sequence of 1st, 2nd, and 3rd returns, with a last large pulse representing the ground. The ability to separate these returns and record them as a point cloud makes it possible for the creation of precise terrain models.
Once an 3D map of the surroundings is created and the robot is able to navigate using this data. This involves localization, constructing the path needed to get to a destination,’ and dynamic obstacle detection. This is the process that identifies new obstacles not included in the map’s original version and updates 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 identify its location relative to that map. Engineers use this information for a range of tasks, including planning routes and obstacle detection.
To enable SLAM to work it requires sensors (e.g. the laser or camera), and a computer that has the right software to process the data. Also, you will require an IMU to provide basic information about your position. The system will be able to track your robot’s exact location in an undefined environment.
The SLAM system is complicated and there are a variety of back-end options. No matter which one you choose for your SLAM system, a successful SLAM system requires a constant interplay between the range measurement device and the software that extracts the data and the vehicle or robot. This is a highly dynamic process that has an almost endless amount of variance.
As the robot moves, it adds scans to its map. The SLAM algorithm analyzes these scans against previous ones by making use of a process known as scan matching. This helps to establish loop closures. The SLAM algorithm updates its robot’s estimated trajectory when a loop closure has been identified.
The fact that the surroundings can change over time is a further factor that makes it more difficult for SLAM. For example, if your robot walks down an empty aisle at one point, and is then confronted by pallets at the next spot it will have a difficult time finding these two points on its map. This is where handling dynamics becomes critical, and this is a typical characteristic of the modern Lidar SLAM algorithms.
Despite these issues, a properly configured SLAM system is incredibly effective for navigation and 3D scanning. It is especially useful in environments that don’t let the robot depend on GNSS for positioning, like an indoor factory floor. It is important to keep in mind that even a properly-configured SLAM system can be prone to errors. It is crucial to be able to detect these issues and comprehend how they affect the SLAM process to fix them.
Mapping
The mapping function creates a map of the robot’s surrounding, which includes the robot itself as well as its wheels and actuators and everything else that is in its view. This map is used for localization, path planning, and obstacle detection. This is an area in which 3D Lidars are especially helpful because they can be regarded as a 3D Camera (with one scanning plane).
The process of building maps can take some time, but the results pay off. The ability to create a complete, consistent map of the robot’s surroundings allows it to conduct high-precision navigation as well as navigate around obstacles.
As a rule of thumb, the greater resolution of the sensor, the more precise the map will be. Not all robots require high-resolution maps. For example, a floor sweeping robot might not require the same level of detail as a robotic system for industrial use navigating large factories.
There are a variety of mapping algorithms that can be utilized with LiDAR sensors. One of the most well-known algorithms is Cartographer which utilizes two-phase pose graph optimization technique to correct for drift and maintain a uniform global map. It is particularly effective when used in conjunction with the odometry.
Another alternative is GraphSLAM which employs a system of linear equations to model the constraints of a graph. The constraints are modeled as an O matrix and an one-dimensional X vector, each vertice of the O matrix containing the distance to a point on the X vector. A GraphSLAM Update is a series of subtractions and additions to these matrix elements. The result is that all the O and X Vectors are updated in order to reflect the latest observations made by the robot.
SLAM+ is another useful mapping algorithm that combines odometry and mapping using an Extended Kalman filter (EKF). The EKF updates not only the uncertainty of the robot’s current position, but also the uncertainty of the features that have been drawn 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 should be able to perceive its environment so that it can avoid obstacles and get to its destination. It uses sensors such as digital cameras, infrared scans sonar and laser radar to determine the surrounding. Additionally, it employs inertial sensors to measure its speed and position, as well as its orientation. These sensors enable it to navigate without danger and avoid collisions.
One of the most important aspects of this process is obstacle detection that consists of the use of an IR range sensor to measure the distance between the robot and the obstacles. The sensor can be mounted to the vehicle, the robot or a pole. It is crucial to remember that the sensor can be affected by a myriad of factors, including wind, rain and fog. Therefore, it is essential to calibrate the sensor prior to each use.
The results of the eight neighbor cell clustering algorithm can be used to identify static obstacles. This method isn’t very precise due to the occlusion caused by the distance between laser lines and the camera’s angular speed. To overcome this problem, multi-frame fusion was used to improve the accuracy of static obstacle detection.
The method of combining roadside unit-based as well as obstacle detection using a vehicle camera has been proven to improve the efficiency of processing data and reserve redundancy for lidar Robot Navigation further navigational tasks, like path planning. The result of this technique is a high-quality picture of the surrounding environment that is more reliable than one frame. The method has been tested with other obstacle detection methods including YOLOv5 VIDAR, YOLOv5, and monocular ranging in outdoor comparison experiments.
The results of the test proved that the algorithm was able accurately determine the height and location of an obstacle, in addition to its rotation and tilt. It also had a great ability to determine the size of obstacles and its color. The method also showed excellent stability and durability, even in the presence of moving obstacles.