LiDAR and Robot Navigation
LiDAR is one of the central capabilities needed for mobile robots to navigate safely. It has a variety of functions, including obstacle detection and route planning.
2D lidar scans the surroundings in a single plane, which is simpler and less expensive than 3D systems. This makes for an enhanced system that can detect obstacles even if they're not aligned perfectly with the sensor plane.
LiDAR Device
LiDAR sensors (Light Detection And Ranging) utilize laser beams that are safe for the eyes to "see" their environment. By sending out light pulses and measuring the amount of time it takes to return each pulse the systems are able to determine distances between the sensor and the objects within its field of vision. The data is then compiled into an intricate, real-time 3D representation of the area being surveyed. This is known as a point cloud.
The precise sensing capabilities of LiDAR allows robots to have a comprehensive understanding of their surroundings, empowering them with the confidence to navigate through various scenarios. The technology is particularly adept in pinpointing precise locations by comparing the data with maps that exist.
LiDAR devices vary depending on the application they are used for in terms of frequency (maximum range) and resolution, as well as horizontal field of vision. However, the fundamental principle is the same for all models: the sensor transmits an optical pulse that strikes the surrounding environment before returning to the sensor. This process is repeated thousands of times every second, creating an immense collection of points that make up the area that is surveyed.
Each return point is unique and is based on the surface of the object reflecting the pulsed light. For example trees and buildings have different reflectivity percentages than water or bare earth. The intensity of light also varies depending on the distance between pulses as well as the scan angle.
The data is then processed to create a three-dimensional representation. the point cloud, which can be viewed using an onboard computer for navigational reasons. The point cloud can be filtered so that only the area you want to see is shown.
The point cloud can be rendered in color by matching reflected light to transmitted light. This results in a better visual interpretation, as well as an improved spatial analysis. The point cloud can be labeled with GPS data, which allows for accurate time-referencing and temporal synchronization. This is useful for quality control, and time-sensitive analysis.
LiDAR is a tool that can be utilized in a variety of industries and applications. It is used by 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, assisting researchers to assess the biomass and carbon sequestration capabilities. Other uses include environmental monitoring and detecting changes in atmospheric components, such as CO2 or greenhouse gases.
Range Measurement Sensor
A LiDAR device is a range measurement device that emits laser pulses continuously towards surfaces and objects. This pulse is reflected and the distance to the surface or object can be determined by measuring the time it takes for the pulse to be able to reach the object before returning to the sensor (or the reverse). The sensor is typically mounted on a rotating platform to ensure that measurements of range are taken quickly over a full 360 degree sweep. These two-dimensional data sets offer a complete overview of the robot's surroundings.
There are many different types of range sensors and they have varying minimum and maximal ranges, resolutions and fields of view. KEYENCE offers a wide variety of these sensors and will help you choose the right solution for your particular needs.
Range data can be used to create contour maps within two dimensions of the operating area. It can be combined with other sensor technologies, such as cameras or vision systems to increase the performance and durability of the navigation system.
Cameras can provide additional data in the form of images to assist in the interpretation of range data, and also improve the accuracy of navigation. lidar robot vacuum and mop are designed to utilize range data as input to computer-generated models of the environment that can be used to guide the robot by interpreting what it sees.
To make the most of the LiDAR system it is crucial to have a thorough understanding of how the sensor functions and what it is able to do. The robot will often be able to move between two rows of plants and the objective is to determine the right one using the LiDAR data.
A technique known as simultaneous localization and mapping (SLAM) is a method to accomplish this. SLAM is an iterative method that uses a combination of known conditions such as the robot’s current position and direction, as well as modeled predictions based upon its speed and head, sensor data, with estimates of noise and error quantities and then iteratively approximates a result to determine the robot’s location and pose. With this method, the robot can navigate in 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 ability to build a map of its environment and pinpoint its location within the map. Its development is a major research area for robotics and artificial intelligence. This paper surveys a number of leading approaches for solving the SLAM issues and discusses the remaining problems.
The main goal of SLAM is to calculate the robot's movements in its surroundings while creating a 3D map of the surrounding area. SLAM algorithms are built on the features derived from sensor information, which can either be laser or camera data. These features are defined as points of interest that are distinct from other objects. These features could be as simple or complex 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 larger field of view permits the sensor to capture a larger area of the surrounding environment. This can result in an improved navigation accuracy and a more complete map of the surrounding.
To accurately estimate the robot's position, the SLAM algorithm must match point clouds (sets of data points in space) from both the previous and present environment. This can be achieved using a number of algorithms that include the iterative closest point and normal distributions transformation (NDT) methods. These algorithms can be merged with sensor data to produce a 3D map of the surrounding that can be displayed as an occupancy grid or a 3D point cloud.
A SLAM system is complex and requires significant processing power in order to function efficiently. This is a problem for robotic systems that need to run in real-time, or run on an insufficient hardware platform. To overcome these challenges a SLAM can be adapted to the sensor hardware and software. For instance a laser scanner with a high resolution and wide FoV may require more resources than a less expensive low-resolution scanner.
Map Building
A map is an illustration of the surroundings, typically in three dimensions, which serves a variety of functions. It can be descriptive, indicating the exact location of geographical features, and is used in various applications, like a road map, or exploratory, looking for patterns and connections between various phenomena and their properties to uncover deeper meaning to a topic, such as many thematic maps.
Local mapping is a two-dimensional map of the surrounding area by using LiDAR sensors located at the bottom of a robot, just above the ground level. To do this, the sensor provides distance information derived from a line of sight to each pixel of the two-dimensional range finder, which allows for topological modeling of the surrounding space. Most navigation and segmentation algorithms are based on this data.
Scan matching is the algorithm that takes advantage of the distance information to calculate an estimate of orientation and position for the AMR for each time point. This is achieved by minimizing the differences between the robot's anticipated future state and its current condition (position or rotation). Scanning match-ups can be achieved by using a variety of methods. Iterative Closest Point is the most popular method, and has been refined many times over the time.
Another approach to local map construction is Scan-toScan Matching. This is an algorithm that builds incrementally that is used when the AMR does not have a map or the map it does have doesn't closely match its current environment due to changes in the environment. This approach is susceptible to long-term drift in the map, since the accumulated corrections to position and pose are susceptible to inaccurate updating over time.
To overcome this problem, a multi-sensor fusion navigation system is a more robust approach that utilizes the benefits of different types of data and overcomes the weaknesses of each of them. This kind of navigation system is more resilient to the erroneous actions of the sensors and is able to adapt to changing environments.