LiDAR and Robot Navigation
LiDAR is one of the central capabilities needed for mobile robots to safely navigate. It has a variety of functions, such as obstacle detection and route planning.
2D lidar scans the environment in a single plane, making it easier and more economical than 3D systems. This creates a powerful system that can detect objects even if they're not perfectly aligned with the sensor plane.
LiDAR Device
LiDAR sensors (Light Detection And Ranging) utilize laser beams that are safe for eyes to "see" their surroundings. By transmitting light pulses and observing the time it takes to return each pulse they can calculate distances between the sensor and the objects within its field of view. The data is then compiled to create a 3D real-time representation of the region being surveyed known as a "point cloud".
LiDAR's precise sensing ability gives robots an in-depth knowledge of their environment which gives them the confidence to navigate through various situations. Accurate localization is a particular benefit, since the technology pinpoints precise locations based on cross-referencing data with maps that are already in place.
The LiDAR technology varies based on their application in terms of frequency (maximum range) and resolution as well as horizontal field of vision. However, the basic principle is the same across all models: the sensor sends a laser pulse that hits the surrounding environment and returns to the sensor. This process is repeated a thousand times per second, leading to an enormous collection of points that make up the area that is surveyed.
Each return point is unique due to the composition of the object reflecting the light. Buildings and trees, for example, have different reflectance percentages than the bare earth or water. The intensity of light varies depending on the distance between pulses as well as the scan angle.
The data is then assembled into an intricate, three-dimensional representation of the surveyed area known as a point cloud which can be seen by a computer onboard for navigation purposes. The point cloud can also be reduced to display only the desired area.
The point cloud can be rendered in color by comparing reflected light with transmitted light. This will allow for better visual interpretation and more precise spatial analysis. The point cloud can be labeled with GPS data, which allows for accurate time-referencing and temporal synchronization. This is beneficial for quality control, and time-sensitive analysis.
LiDAR is employed in a myriad of applications and industries. It is used on drones to map topography, and for forestry, and on autonomous vehicles that produce an electronic map for safe navigation. It is also used to determine the vertical structure of forests, helping researchers to assess the carbon sequestration capacities and biomass. Other uses include environmental monitors and detecting changes to atmospheric components like CO2 and greenhouse gases.
Range Measurement Sensor
The core of a LiDAR device is a range sensor that continuously emits a laser signal towards surfaces and objects. The pulse is reflected back and the distance to the object or surface can be determined by measuring the time it takes the beam to reach the object and then return to the sensor (or vice versa). The sensor is usually placed on a rotating platform so that range measurements are taken rapidly across a 360 degree sweep. These two-dimensional data sets give a clear view of the robot's surroundings.
There are a variety of range sensors and they have different minimum and maximum ranges, resolutions, and fields of view. KEYENCE offers a wide range of these sensors and will help you choose the right solution for your needs.
Range data is used to generate two dimensional contour maps of the operating area. It can be combined with other sensors such as cameras or vision system to enhance the performance and robustness.
Adding cameras to the mix adds additional visual information that can be used to help in the interpretation of range data and to improve the accuracy of navigation. Certain vision systems utilize range data to construct a computer-generated model of the environment, which can be used to guide robots based on their observations.
To get the most benefit from the LiDAR sensor, it's essential to have a thorough understanding of how the sensor operates and what it is able to do. In most cases the robot will move between two crop rows and the objective is to identify the correct row using the LiDAR data set.
A technique known as simultaneous localization and mapping (SLAM) can be employed to achieve this. SLAM is a iterative algorithm which uses a combination known conditions such as the robot’s current location and direction, modeled forecasts based upon its speed and head speed, as well as other sensor data, and estimates of error and noise quantities, and iteratively approximates a result to determine the robot’s location and pose. This technique lets the robot move through unstructured and complex areas without the need for markers or reflectors.
SLAM (Simultaneous Localization & Mapping)
The SLAM algorithm is crucial to a robot's capability to create a map of its environment and localize it within that map. Its evolution is a major research area for robotics and artificial intelligence. This paper reviews a variety of current approaches to solve the SLAM problems and outlines the remaining challenges.
SLAM's primary goal is to calculate the sequence of movements of a robot in its surroundings and create an 3D model of the environment. The algorithms of SLAM are based upon features derived from sensor data which could be camera or laser data. These characteristics are defined by points or objects that can be identified. They could be as basic as a plane or corner or more complicated, such as shelving units or pieces of equipment.
Most Lidar sensors have a limited field of view (FoV) which could limit the amount of information that is available to the SLAM system. best budget lidar robot vacuum allow the sensor to capture a greater portion of the surrounding area, which can allow for more accurate map of the surroundings and a more precise navigation system.
In order to accurately determine the robot's position, an SLAM algorithm must match point clouds (sets of data points scattered across space) from both the previous and present environment. This can be done using a number of algorithms that include the iterative closest point and normal distributions transformation (NDT) methods. These algorithms can be paired with sensor data to produce a 3D map that can be displayed as an occupancy grid or 3D point cloud.
A SLAM system can be a bit complex and require significant amounts of processing power to operate efficiently. This could pose problems for robotic systems that must achieve real-time performance or run on a small hardware platform. To overcome these difficulties, a SLAM can be tailored to the sensor hardware and software environment. For example, a laser sensor with a high resolution and wide FoV could require more processing resources than a lower-cost and lower resolution scanner.
Map Building
A map is an image of the surrounding environment that can be used for a variety of purposes. It is usually three-dimensional and serves a variety of reasons. It can be descriptive (showing accurate location of geographic features to be used in a variety of ways such as a street map) as well as exploratory (looking for patterns and relationships among phenomena and their properties, to look for deeper meaning in a specific subject, such as in many thematic maps) or even explanatory (trying to convey information about an object or process, often through visualizations like graphs or illustrations).
Local mapping uses the data provided by LiDAR sensors positioned on the bottom of the robot, just above ground level to build a two-dimensional model of the surrounding area. To accomplish this, the sensor will provide distance information from a line sight to each pixel of the two-dimensional range finder which allows for topological modeling of the surrounding space. This information is used to develop typical navigation and segmentation algorithms.
Scan matching is an algorithm that makes use of distance information to determine the location and orientation of the AMR for each time point. This is accomplished by minimizing the gap between the robot's future state and its current state (position, rotation). A variety of techniques have been proposed to achieve scan matching. The most popular one is Iterative Closest Point, which has seen numerous changes over the years.
Another method for achieving local map construction is Scan-toScan Matching. This incremental algorithm is used when an AMR doesn't have a map, or the map that it does have does not coincide with its surroundings due to changes. This approach is very vulnerable to long-term drift in the map, as the cumulative position and pose corrections are susceptible to inaccurate updates over time.
A multi-sensor fusion system is a robust solution that uses multiple data types to counteract the weaknesses of each. This type of system is also more resistant to the smallest of errors that occur in individual sensors and is able to deal with dynamic environments that are constantly changing.