LiDAR and Robot Navigation
LiDAR is a crucial feature for mobile robots that need to be able to navigate in a safe manner. It can perform a variety of functions, including obstacle detection and route planning.
2D lidar scans the surrounding in one plane, which is simpler and cheaper than 3D systems. This makes it a reliable system that can detect objects even if they're perfectly aligned with the sensor plane.
LiDAR Device
LiDAR sensors (Light Detection And Ranging) use laser beams that are safe for the eyes to "see" their surroundings. By transmitting pulses of light and observing the time it takes for each returned pulse, these systems are able to determine distances between the sensor and the objects within its field of vision. The information is then processed into a complex 3D model that is real-time and in real-time the surveyed area known as a point cloud.
The precise sense of LiDAR provides robots with an extensive understanding of their surroundings, equipping them with the ability to navigate through various scenarios. Accurate localization is a major strength, as the technology pinpoints precise positions using cross-referencing of data with maps already in use.
LiDAR devices vary depending on the application they are used for in terms of frequency (maximum range), resolution and horizontal field of vision. The basic principle of all LiDAR devices is the same: the sensor sends out a laser pulse which hits the surrounding area and then returns to the sensor. The process repeats thousands of times per second, resulting in an enormous collection of points that represent the surveyed area.
Each return point is unique, based on the composition of the surface object reflecting the pulsed light. For example, trees and buildings have different reflective percentages than water or bare earth. The intensity of light varies with the distance and scan angle of each pulsed pulse as well.
This data is then compiled into an intricate, three-dimensional representation of the surveyed area - called a point cloud which can be seen on an onboard computer system to assist in navigation. The point cloud can also be filtered to display only the desired area.
The point cloud could be rendered in true color by matching the reflection of light to the transmitted light. This results in a better visual interpretation, as well as an accurate spatial analysis. The point cloud can also be marked with GPS information that allows for accurate time-referencing and temporal synchronization, useful for quality control and time-sensitive analysis.
LiDAR is utilized in a wide range of industries and applications. It is used on drones used for topographic mapping and forestry work, as well as on autonomous vehicles to create a digital map of their surroundings to ensure safe navigation. robot vacuum cleaner with lidar can also be utilized to measure the vertical structure of forests, which helps researchers assess biomass and carbon sequestration capabilities. Other applications include monitoring the environment and detecting changes to atmospheric components like CO2 and greenhouse gases.
Range Measurement Sensor
The heart of a LiDAR device is a range sensor that emits a laser signal towards surfaces and objects. The laser pulse is reflected, and the distance to the surface or object can be determined by measuring the time it takes for the pulse to reach the object and then return to the sensor (or the reverse). The sensor is usually mounted on a rotating platform, so that measurements of range are taken quickly over a full 360 degree sweep. These two-dimensional data sets offer an exact image of the robot's surroundings.
There are different types of range sensor and they all have different minimum and maximum ranges. They also differ in their resolution and field. KEYENCE offers a wide range of sensors available and can help you choose the most suitable one for your needs.
Range data is used to create two dimensional contour maps of the area of operation. It can be paired with other sensor technologies such as cameras or vision systems to improve efficiency and the robustness of the navigation system.
The addition of cameras can provide additional data in the form of images to aid in the interpretation of range data and increase the accuracy of navigation. Certain vision systems utilize range data to build a computer-generated model of environment, which can then be used to guide the robot based on its observations.
To make the most of a LiDAR system it is crucial to have a thorough understanding of how the sensor operates and what it can accomplish. The robot is often able to shift between two rows of crops and the objective is to identify the correct one by using LiDAR data.
A technique known as simultaneous localization and mapping (SLAM) can be employed to accomplish this. SLAM is an iterative algorithm that uses a combination of known conditions, such as the robot's current location and direction, modeled predictions on the basis of its current speed and head, as well as sensor data, with estimates of error and noise quantities and then iteratively approximates a result to determine the robot's position and location. This technique lets the robot move in unstructured and complex environments without the use of reflectors or markers.
SLAM (Simultaneous Localization & Mapping)
The SLAM algorithm plays a crucial role in a robot's ability to map its environment and locate itself within it. Its evolution is a major research area for artificial intelligence and mobile robots. This paper reviews a variety of leading approaches for solving the SLAM issues and discusses the remaining problems.
The primary objective of SLAM is to calculate the sequence of movements of a robot in its environment, while simultaneously creating an accurate 3D model of that environment. The algorithms used in SLAM are based upon features derived from sensor data that could be camera or laser data. These features are defined as objects or points of interest that can be distinguished from others. These features can be as simple or complicated as a corner or plane.
The majority of Lidar sensors only have limited fields of view, which may restrict the amount of data available to SLAM systems. A wide FoV allows for the sensor to capture a greater portion of the surrounding environment which allows for more accurate map of the surroundings and a more precise navigation system.
To accurately estimate the location of the robot, the SLAM must be able to match point clouds (sets of data points) from the present and previous environments. There are a variety of algorithms that can be used for this purpose such as iterative nearest point and normal distributions transform (NDT) methods. These algorithms can be used in conjunction with sensor data to create an 3D map, which can then 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 in order to function efficiently. This could pose challenges for robotic systems that must perform in real-time or on a tiny hardware platform. To overcome these challenges a SLAM can be optimized to the sensor hardware and software. For instance a laser scanner with high resolution and a wide FoV could require more processing resources than a cheaper low-resolution scanner.
Map Building
A map is an image of the world that can be used for a number of purposes. It is usually three-dimensional and serves many different reasons. It could be descriptive (showing exact locations of geographical features for use in a variety of applications such as street maps) or exploratory (looking for patterns and relationships between phenomena and their properties to find deeper meanings in a particular subject, like many thematic maps), or even explanatory (trying to convey details about an object or process, often using visuals, such as graphs or illustrations).
Local mapping utilizes the information generated by LiDAR sensors placed at the base of the robot, just above the ground to create an image of the surrounding. To do this, the sensor will provide distance information from a line of sight to each pixel of the two-dimensional range finder, which allows topological models of the surrounding space. This information is used to develop normal segmentation and navigation algorithms.
Scan matching is an algorithm that utilizes the distance information to compute a position and orientation estimate for the AMR for each time point. This is accomplished by reducing the error of the robot's current state (position and rotation) and its expected future state (position and orientation). A variety of techniques have been proposed to achieve scan matching. Iterative Closest Point is the most popular, and has been modified several times over the time.
Another approach to local map building is Scan-to-Scan Matching. This is an incremental algorithm that is employed when the AMR does not have a map or the map it does have doesn't closely match its current surroundings due to changes in the surrounding. This method is extremely susceptible to long-term map drift because the accumulation of pose and position corrections are susceptible to inaccurate updates over time.
To address this issue To overcome this problem, a multi-sensor navigation system is a more robust solution that utilizes the benefits of different types of data and counteracts the weaknesses of each of them. This type of system is also more resistant to the flaws in individual sensors and is able to deal with the dynamic environment that is constantly changing.