LiDAR and Robot Navigation
LiDAR is among the central capabilities needed for mobile robots to safely navigate. It has a variety of functions, including obstacle detection and route planning.
2D lidar scans an area in a single plane, making it easier and more cost-effective compared to 3D systems. This makes for a more robust system that can identify obstacles even if they're not aligned perfectly with the sensor plane.
LiDAR Device
LiDAR (Light Detection and Ranging) sensors use eye-safe laser beams to "see" the environment around them. By transmitting light pulses and measuring the time it takes for each returned pulse they are able to calculate distances between the sensor and objects in its field of view. The data is then assembled to create a 3-D, real-time representation of the surveyed region called"point cloud" "point cloud".
The precise sensing prowess of LiDAR allows robots to have a comprehensive understanding of their surroundings, providing them with the ability to navigate through various scenarios. The technology is particularly good at determining precise locations by comparing the data with maps that exist.
LiDAR devices vary depending on their use in terms of frequency (maximum range) and resolution as well as horizontal field of vision. But the principle is the same across all models: the sensor sends a laser pulse that hits the surrounding environment before returning to the sensor. The process repeats thousands of times per second, resulting in a huge collection of points representing the area being surveyed.
Each return point is unique based on the structure of the surface reflecting the light. Trees and buildings, for example, have different reflectance percentages than bare earth or water. Light intensity varies based on the distance and the scan angle of each pulsed pulse.
The data is then compiled to create a three-dimensional representation, namely a point cloud, which can be viewed by an onboard computer to aid in navigation. The point cloud can be filtered so that only the desired area is shown.
Or, the point cloud could be rendered in a true color by matching the reflection of light to the transmitted light. This will allow for better visual interpretation and more accurate spatial analysis. The point cloud can be labeled with GPS data, which permits precise time-referencing and temporal synchronization. This is helpful for quality control, and time-sensitive analysis.
LiDAR is employed in a variety of applications and industries. It is utilized on drones to map topography and for forestry, and on autonomous vehicles that produce an electronic map for safe navigation. It can also be used to measure the vertical structure of forests which allows researchers to assess the carbon storage capacity of biomass and carbon sources. Other applications include monitoring the environment and monitoring changes in atmospheric components like CO2 or greenhouse gases.
Range Measurement Sensor
A LiDAR device consists of an array measurement system that emits laser pulses repeatedly toward objects and surfaces. The laser pulse is reflected, and the distance to the object or surface can be determined by determining how long it takes for the pulse to be able to reach the object before returning to the sensor (or reverse). The sensor is typically mounted 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 view of the robot's surroundings.
There are lidar robot of range sensors. They have varying minimum and maximal ranges, resolutions and fields of view. KEYENCE offers a wide variety of these sensors and can assist you in choosing the best solution for your particular needs.
Range data can be used to create contour maps within two dimensions of the operating space. It can be used in conjunction with other sensors such as cameras or vision system to improve the performance and durability.
Cameras can provide additional information in visual terms to aid in the interpretation of range data and increase navigational accuracy. Certain vision systems utilize range data to build an artificial model of the environment, which can be used to direct the robot based on its observations.
To get the most benefit from the LiDAR sensor, it's essential to have a good understanding of how the sensor functions and what it is able to do. In most cases, the robot is moving between two crop rows and the goal is to determine the right row by using the LiDAR data set.
A technique called simultaneous localization and mapping (SLAM) is a method to accomplish this. SLAM is an iterative algorithm which makes use of the combination of existing circumstances, such as the robot's current location and orientation, modeled predictions using its current speed and heading, sensor data with estimates of error and noise quantities, and iteratively approximates the solution to determine the robot's location and pose. This technique allows the robot to navigate in unstructured and complex environments without the use of reflectors or markers.
SLAM (Simultaneous Localization & Mapping)
The SLAM algorithm plays a key role in a robot's capability to map its environment and locate itself within it. Its evolution is a major research area for robotics and artificial intelligence. This paper surveys a number of current approaches to solve the SLAM problems and outlines the remaining problems.
The main objective of SLAM is to determine the robot's movements in its environment while simultaneously creating a 3D model of the environment. The algorithms used in SLAM are based on features extracted from sensor data, which could be laser or camera data. These features are identified by objects or points that can be distinguished. These features can be as simple or as complex as a plane or corner.
Most Lidar sensors have a small field of view, which may restrict the amount of information available to SLAM systems. Wide FoVs allow the sensor to capture a greater portion of the surrounding environment which can allow for more accurate map of the surrounding area and a more accurate navigation system.
To be able to accurately estimate the robot's position, an SLAM algorithm must match point clouds (sets of data points in space) from both the previous and current environment. This can be accomplished by using a variety of algorithms such as the iterative nearest point and normal distributions transformation (NDT) methods. These algorithms can be used in conjunction with sensor data to produce a 3D map that can be displayed as an occupancy grid or 3D point cloud.
A SLAM system is extremely complex and requires substantial processing power to run efficiently. This can be a problem for robotic systems that need to run in real-time or run on a limited hardware platform. To overcome these issues, a SLAM can be optimized to the hardware of the sensor and software environment. For instance, a laser sensor with high resolution and a wide FoV may require more processing resources than a less expensive low-resolution scanner.
Map Building
A map is an image of the surrounding environment that can be used for a number of reasons. It is typically three-dimensional, and serves a variety of reasons. It can be descriptive (showing exact locations of geographical features that can be used in a variety of applications like street maps) as well as exploratory (looking for patterns and relationships between various phenomena and their characteristics to find deeper meaning in a given subject, like many thematic maps) or even explanational (trying to communicate details about the process or object, typically through visualisations, like graphs or illustrations).
Local mapping uses the data generated by LiDAR sensors placed at the base of the robot just above ground level to build a two-dimensional 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, which allows topological modeling of the surrounding space. Most navigation and segmentation algorithms are based on this data.
Scan matching is an algorithm that uses distance information to estimate the position and orientation of the AMR for each time point. This is done by minimizing the error of the robot's current state (position and rotation) and the expected future state (position and orientation). Scanning matching can be accomplished by using a variety of methods. Iterative Closest Point is the most well-known technique, and has been tweaked numerous times throughout the years.
Another approach to local map building is Scan-to-Scan Matching. This algorithm works 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 approach is vulnerable to long-term drifts in the map, as the cumulative corrections to location and pose are subject to inaccurate updating over time.
To overcome this problem to overcome this issue, a multi-sensor fusion navigation system is a more robust solution that makes use of the advantages of a variety of data types and counteracts the weaknesses of each one of them. This type of system is also more resistant to the flaws in individual sensors and can deal with dynamic environments that are constantly changing.