• 목록
• 답변
• 글쓰기
• 게시판 리스트 옵션
  • 수정
  • 삭제

LiDAR and Robot Navigation

LiDAR is among the most important capabilities required by mobile robots to navigate safely. It can perform a variety of capabilities, including obstacle detection and route planning.

2D vacuum lidar scans the surroundings in one plane, which is easier and cheaper than 3D systems. This allows for a robust system that can recognize objects even if they're completely aligned with the sensor plane.

lidar robot vacuum cleaner Device

LiDAR (Light Detection and Ranging) sensors use eye-safe laser beams to "see" the world around them. By sending out light pulses and measuring the amount of time it takes to return each pulse the systems are able to calculate distances between the sensor and objects within its field of vision. The data is then compiled to create a 3D real-time representation of the region being surveyed known as a "point cloud".

The precise sensing capabilities of LiDAR allows robots to have an knowledge of their surroundings, providing them with the confidence to navigate diverse scenarios. LiDAR is particularly effective at pinpointing precise positions by comparing the data with existing maps.

LiDAR devices vary depending on their use in terms of frequency (maximum range), resolution and horizontal field of vision. But the principle is the same across all models: the sensor transmits the laser pulse, which hits the environment around it and then returns to the sensor. This is repeated thousands per second, resulting in an enormous collection of points that represent the surveyed area.

Each return point is unique, based on the surface object that reflects the pulsed light. For example buildings and trees have different reflective percentages than bare earth or water. The intensity of light is dependent on the distance and scan angle of each pulsed pulse.

The data is then compiled into a complex 3-D representation of the surveyed area - called a point cloud - that can be viewed 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 can also be rendered in color by comparing reflected light with transmitted light. This will allow for better visual interpretation and more accurate analysis of spatial space. The point cloud can be labeled with GPS data that allows for accurate time-referencing and temporal synchronization. This is helpful for quality control and time-sensitive analysis.

lidar vacuum cleaner is employed in a variety of applications and industries. It is found on drones used for topographic mapping and forest work, as well as on autonomous vehicles to create an electronic map of their surroundings for safe navigation. It can also be used to measure the vertical structure in forests which aids researchers in assessing carbon storage capacities and biomass. Other uses include environmental monitoring and monitoring changes in atmospheric components, such as CO2 or greenhouse gases.

Range Measurement Sensor

The core of LiDAR devices is a range measurement sensor that repeatedly emits a laser pulse toward objects and surfaces. The laser beam is reflected and the distance can be measured by observing the amount of time it takes for the laser beam to reach the object or surface and then return to the sensor. The sensor is usually placed on a rotating platform, so that measurements of range are taken quickly across a 360 degree sweep. These two-dimensional data sets offer a complete perspective of the robot vacuum cleaner with lidar's environment.

There are various kinds of range sensor and all of them have different ranges of minimum and maximum. They also differ in the resolution and field. KEYENCE has a range of sensors that are available and can help you select the most suitable one for your application.

Range data can be used to create contour maps in two dimensions of the operating space. 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 visual data to aid in the interpretation of range data and improve navigational accuracy. Certain vision systems are designed to utilize range data as input into an algorithm that generates a model of the environment that can be used to guide the robot according to what it perceives.

To make the most of a lidar robot navigation system, it's essential to have a good understanding of how the sensor operates and what it can accomplish. In most cases, the robot is moving between two rows of crop and the goal is to determine the right row by using the LiDAR data sets.

A technique known as simultaneous localization and mapping (SLAM) can be used to achieve this. SLAM is a iterative algorithm which uses a combination known circumstances, like the robot's current location and direction, modeled forecasts based upon the current speed and head, as well as sensor data, and estimates of noise and error quantities and iteratively approximates the result to determine the robot’s location and its pose. This method lets the robot move through unstructured and complex areas without the need for reflectors or markers.

SLAM (Simultaneous Localization & Mapping)

The SLAM algorithm plays an important role in a vacuum robot with lidar's ability to map its surroundings and locate itself within it. Its evolution is a major research area for robotics and artificial intelligence. This paper examines a variety of leading approaches to solving the SLAM problem and outlines the challenges that remain.

The primary objective of SLAM is to calculate the robot's movements within its environment while simultaneously constructing an accurate 3D model of that environment. SLAM algorithms are based on characteristics taken from sensor data which could be laser or camera data. These characteristics are defined by the objects or points that can be identified. They could be as basic as a corner or a plane or more complex, for instance, shelving units or pieces of equipment.

Most Lidar sensors have a limited field of view (FoV), which can limit the amount of data available to the SLAM system. A wide field of view permits the sensor to capture an extensive area of the surrounding environment. This could lead to an improved navigation accuracy and a more complete map of the surrounding area.

To accurately estimate the robot's location, a SLAM must match point clouds (sets of data points) from both the present and the previous environment. This can be accomplished by using a variety of algorithms that include the iterative closest point and normal distributions transformation (NDT) methods. These algorithms can be combined 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 can be complex and require significant amounts of processing power to operate efficiently. This can be a challenge for robotic systems that require to perform in real-time or operate on an insufficient hardware platform. To overcome these issues, the SLAM system can be optimized to the specific software and hardware. For instance a laser sensor with an extremely high resolution and a large FoV may require more resources than a less expensive and lower resolution scanner.

Map Building

A map is a representation of the environment, typically in three dimensions, that serves many purposes. It can be descriptive, displaying the exact location of geographical features, and is used in various applications, like a road map, or an exploratory, looking for patterns and connections between phenomena and their properties to find deeper meaning in a subject like many thematic maps.

Local mapping builds a 2D map of the surrounding area by using LiDAR sensors that are placed at the base of a robot, just above the ground level. To accomplish this, the sensor will provide distance information derived from a line of sight of each pixel in the two-dimensional range finder which allows for topological modeling of the surrounding space. This information is used to create normal segmentation and navigation algorithms.

Scan matching is the algorithm that utilizes the distance information to compute an estimate of orientation and position for the AMR for each time point. This is accomplished by minimizing the gap between the robot's expected future state and its current one (position, rotation). Scanning matching can be accomplished by using a variety of methods. Iterative Closest Point is the most well-known method, and has been refined numerous times throughout the time.

Scan-toScan Matching is another method to achieve local map building. This is an incremental algorithm that is employed when the AMR does not have a map, or the map it does have is not in close proximity to 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.

A multi-sensor system of fusion is a sturdy solution that makes use of different types of data to overcome the weaknesses of each. This type of navigation system is more resistant to the erroneous actions of the sensors and can adapt to changing environments.