LiDAR and Robot Navigation
LiDAR is an essential feature for mobile robots that need to navigate safely. It can perform a variety of functions, including obstacle detection and route planning.
2D lidar scans the environment in one plane, which is easier and cheaper 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) make use of laser beams that are safe for the eyes to "see" their surroundings. By transmitting light pulses and observing the time it takes to return each pulse they are able to determine distances between the sensor and objects within their field of view. The data is then compiled into an intricate 3D model that is real-time and in real-time the area that is surveyed, referred to as a point cloud.
The precise sensing prowess of LiDAR gives robots an extensive knowledge of their surroundings, empowering them with the confidence to navigate through various scenarios. Accurate localization is a major strength, as the technology pinpoints precise locations based on cross-referencing data with existing maps.
Based on the purpose the LiDAR device can differ in terms of frequency and range (maximum distance), resolution, and horizontal field of view. However, the fundamental principle is the same for all models: the sensor emits a laser pulse that hits the surrounding environment and returns to the sensor. The process repeats thousands of times per second, resulting in an immense collection of points that represent the area being surveyed.
Each return point is unique and is based on the surface of the object reflecting the pulsed light. For example buildings and trees have different reflective percentages than bare earth or water. The intensity of light varies with the distance and the scan angle of each pulsed pulse as well.
lidar robot is then processed to create a three-dimensional representation - an image of a point cloud. This can be viewed by an onboard computer for navigational purposes. The point cloud can be filtered to ensure that only the area you want to see is shown.
The point cloud can also be rendered in color by matching reflected light with transmitted light. This results in a better visual interpretation as well as an accurate spatial analysis. The point cloud may also be marked with GPS information that provides temporal synchronization and accurate time-referencing that is beneficial for quality control and time-sensitive analyses.
LiDAR is used in many different industries and applications. It is utilized on drones to map topography, and for forestry, and on autonomous vehicles that create a digital map for safe navigation. It can also be used to determine the vertical structure of forests which aids researchers in assessing biomass and carbon storage capabilities. Other applications include monitoring the environment and detecting changes to atmospheric components such as CO2 or greenhouse gases.
Range Measurement Sensor
The core of LiDAR devices is a range sensor that continuously emits a laser pulse toward surfaces and objects. The laser pulse is reflected and the distance can be determined by observing the time it takes for the laser's pulse to be able to reach the object's surface and then return to the sensor. Sensors are mounted on rotating platforms to enable rapid 360-degree sweeps. Two-dimensional data sets provide an exact picture of the robot’s surroundings.
There are different types of range sensor and they all have different ranges of minimum and maximum. They also differ in the field of view and resolution. KEYENCE has a variety of sensors available and can help you choose the right one for your needs.
Range data is used to create two-dimensional contour maps of the operating area. It can be used in conjunction with other sensors, such as cameras or vision systems to increase the efficiency and robustness.
In addition, adding cameras can provide additional visual data that can be used to help with the interpretation of the range data and increase the accuracy of navigation. Some vision systems use range data to build a computer-generated model of the environment, which can be used to direct a robot based on its observations.
It is essential to understand how a LiDAR sensor operates and what the system can do. The robot is often able to shift between two rows of crops and the aim is to determine the right one using the LiDAR data.
A technique called simultaneous localization and mapping (SLAM) is a method to achieve this. SLAM is an iterative method that uses a combination of known conditions such as the robot’s current location and direction, modeled predictions that are based on its current speed and head, sensor data, and estimates of error and noise quantities and then iteratively approximates a result to determine the robot's location and pose. With this method, the robot will be able to navigate through complex and unstructured environments without the requirement for reflectors or other markers.
SLAM (Simultaneous Localization & Mapping)
The SLAM algorithm is key to a robot's capability to create a map of their surroundings and locate it within that map. Its development is a major research area for artificial intelligence and mobile robots. This paper reviews a range of leading approaches to solving the SLAM problem and outlines the issues that remain.
The main goal of SLAM is to determine the robot's movements in its surroundings while creating a 3D model of the surrounding area. The algorithms used in SLAM are based upon features derived from sensor information, which can either be laser or camera data. These characteristics are defined by points or objects that can be distinguished. These features can be as simple or complex as a plane or corner.
The majority of Lidar sensors have only an extremely narrow field of view, which may restrict the amount of information available to SLAM systems. A wider field of view allows the sensor to capture an extensive area of the surrounding area. This can result in a more accurate navigation and a full mapping of the surrounding area.
To accurately determine the robot's location, an SLAM algorithm must match point clouds (sets of data points scattered across space) from both the current and previous environment. This can be accomplished using a number of algorithms such as the iterative nearest point and normal distributions transformation (NDT) methods. These algorithms can be fused with sensor data to produce an 3D map of the environment, which can be displayed in the form of an occupancy grid or a 3D point cloud.
A SLAM system may be complicated and requires a lot of processing power to function efficiently. This can be a problem for robotic systems that require to perform in real-time or run on a limited hardware platform. To overcome these difficulties, a SLAM can be tailored to the hardware of the sensor and software. For example, a laser scanner with an extensive FoV and a high resolution might require more processing power than a less scan with a lower resolution.
Map Building
A map is an image of the world that can be used for a variety of purposes. It is usually three-dimensional and serves a variety of purposes. It can be descriptive, showing the exact location of geographical features, for use in various applications, such as the road map, or exploratory seeking out patterns and connections between various phenomena and their properties to discover deeper meaning in a subject, such as many thematic maps.
Local mapping uses the data that LiDAR sensors provide at the base of the robot slightly above ground level to construct a 2D model of the surroundings. This is done by the sensor providing distance information from the line of sight of every pixel of the rangefinder in two dimensions that allows topological modeling of surrounding space. The most common segmentation and navigation algorithms are based on this data.
Scan matching is an algorithm that takes advantage of the distance information to calculate a position and orientation estimate for the AMR at each time point. This is accomplished by minimizing the differences between the robot's future state and its current state (position, rotation). Scanning matching can be accomplished by using a variety of methods. The most popular one is Iterative Closest Point, which has seen numerous changes over the years.
Scan-toScan Matching is yet another method to build a local map. This incremental algorithm is used when an AMR doesn't have a map or the map that it does have doesn't coincide with its surroundings due to changes. This method is susceptible to a long-term shift in the map since the cumulative corrections to position and pose are susceptible to inaccurate updating over time.
To overcome this issue, a multi-sensor fusion navigation system is a more robust approach that makes use of the advantages of a variety of data types and overcomes the weaknesses of each one of them. This type of navigation system is more tolerant to the errors made by sensors and is able to adapt to changing environments.