10 Failing Answers To Common Lidar Robot Navigation Questions: Do You Know The Right Ones?
LiDAR and Robot Navigation
LiDAR is among the most important capabilities required by mobile robots to navigate safely. It offers a range of functions such as obstacle detection and path planning.
2D lidar scans an area in a single plane, making it more simple and cost-effective compared to 3D systems. This creates a more robust system that can detect obstacles even when they aren't aligned with the sensor plane.
LiDAR Device
LiDAR sensors (Light Detection and Ranging) utilize laser beams that are safe for eyes to "see" their environment. These systems calculate distances by sending out pulses of light, and then calculating the amount of time it takes for each pulse to return. This data is then compiled into a complex 3D representation that is in real-time. the surveyed area known as a point cloud.
The precise sensing capabilities of LiDAR give robots a deep understanding of their surroundings which gives them the confidence to navigate through various situations. The technology is particularly good in pinpointing precise locations by comparing the data with existing maps.
Robot Vacuum Mops differ based on their use 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 emits the laser pulse, which hits the surrounding environment before returning to the sensor. This is repeated a thousand times per second, leading to an enormous collection of points which represent the area that is surveyed.
Each return point is unique, based on the composition of the surface object reflecting the light. For example buildings and trees have different reflectivity percentages than water or bare earth. The intensity of light varies with the distance and scan angle of each pulsed pulse.
The data is then compiled to create a three-dimensional representation. an image of a point cloud. This can be viewed by an onboard computer to aid in navigation. The point cloud can be filtered to ensure that only the area that is desired is displayed.
The point cloud may also be rendered in color by comparing reflected light to transmitted light. This allows for a more accurate visual interpretation and an accurate spatial analysis. The point cloud can be marked with GPS data, which allows for accurate time-referencing and temporal synchronization. This is beneficial for quality control, and time-sensitive analysis.
LiDAR can be used in many different applications and industries. It is used on drones for topographic mapping and for forestry work, and on autonomous vehicles to create a digital map of their surroundings to ensure safe navigation. It is also utilized to assess the vertical structure of forests which allows researchers to assess carbon storage capacities and biomass. Other applications include environmental monitors and monitoring changes to atmospheric components such as CO2 or greenhouse gases.
Range Measurement Sensor
A LiDAR device is a range measurement device that emits laser pulses continuously toward objects and surfaces. The laser pulse is reflected and the distance can be determined by observing the time it takes for the laser beam to reach the surface or object and then return to the sensor. Sensors are mounted on rotating platforms to enable rapid 360-degree sweeps. These two-dimensional data sets offer a detailed picture of the robot’s surroundings.
There are various types of range sensors and they all have different ranges of minimum and maximum. They also differ in their field of view and resolution. KEYENCE has a range of sensors that are available and can help you select the right one for your application.
Range data is used to create two dimensional contour maps of the area of operation. It can be paired with other sensor technologies like cameras or vision systems to enhance the performance and robustness of the navigation system.
Cameras can provide additional information in visual terms to aid in the interpretation of range data and improve the accuracy of navigation. Certain vision systems utilize range data to build a computer-generated model of environment, which can be used to direct the robot based on its observations.
To get the most benefit from a LiDAR system it is crucial to have a thorough understanding of how the sensor operates and what it is able to do. The robot will often shift between two rows of plants and the goal is to find the correct one by using LiDAR data.
To accomplish this, a method called simultaneous mapping and locatation (SLAM) is a technique that can be utilized. SLAM is an iterative algorithm that makes use of a combination of known conditions, such as the robot's current position and orientation, as well as modeled predictions that are based on the current speed and direction sensors, and estimates of error and noise quantities and iteratively approximates a solution to determine the robot's position and position. Using this method, the robot can move through unstructured and complex environments without the necessity of reflectors or other markers.
SLAM (Simultaneous Localization & Mapping)
The SLAM algorithm is the key to a robot's ability create a map of their environment and pinpoint its location within that map. Its evolution is a major research area in the field of artificial intelligence and mobile robotics. This paper surveys a number of current approaches to solve the SLAM problems and highlights the remaining challenges.
The primary goal of SLAM is to determine the robot's movement patterns in its environment while simultaneously creating a 3D map of the surrounding area. The algorithms used in SLAM are based on features extracted from sensor data that could be laser or camera data. These features are categorized as objects or points of interest that are distinct from other objects. They could be as simple as a plane or corner or more complex, like a shelving unit or piece of equipment.
Most Lidar sensors have a small field of view, which could restrict the amount of data available to SLAM systems. Wide FoVs allow the sensor to capture more of the surrounding area, which could result in more accurate map of the surrounding area and a more accurate navigation system.
To accurately determine the robot's location, the SLAM must be able to match point clouds (sets of data points) from both the present and previous environments. There are a variety of algorithms that can be utilized to achieve this goal, including iterative closest point and normal distributions transform (NDT) methods. These algorithms can be used in conjunction with sensor data to create a 3D map that can later be displayed as an occupancy grid or 3D point cloud.
A SLAM system can be a bit complex and requires a lot of processing power to function efficiently. This could pose challenges for robotic systems which must perform in real-time or on a small hardware platform. To overcome these challenges a SLAM can be optimized to the hardware of the sensor and software. For instance a laser scanner with large FoV and high resolution may require more processing power than a smaller, lower-resolution scan.
Map Building
A map is an illustration of the surroundings generally in three dimensions, and serves a variety of functions. It could be descriptive, displaying the exact location of geographical features, for use in various applications, such as a road map, or an exploratory one, looking for patterns and connections between various phenomena and their properties to discover deeper meaning in a subject like thematic maps.
Local mapping uses the data provided by LiDAR sensors positioned at the bottom of the robot just above ground level to construct an image of the surroundings. To accomplish this, the sensor gives distance information from a line of sight from each pixel in the two-dimensional range finder, which permits topological modeling of the surrounding space. This information is used to design typical navigation and segmentation algorithms.
Scan matching is an algorithm that utilizes distance information to estimate the orientation and position of the AMR for every time point. This is accomplished by minimizing the error of the robot's current condition (position and rotation) and its expected future state (position and orientation). There are a variety of methods to achieve scan matching. Iterative Closest Point is the most popular method, and has been refined many times over the years.
Scan-to-Scan Matching is a different method to create a local map. This algorithm works when an AMR doesn't have a map, or the map it does have doesn't coincide with its surroundings due to changes. This method is extremely susceptible to long-term map drift because the cumulative position and pose corrections are subject to inaccurate updates over time.
A multi-sensor system of fusion is a sturdy solution that uses various data types to overcome the weaknesses of each. This kind of system is also more resilient to the flaws in individual sensors and is able to deal with environments that are constantly changing.