Notes

A Rewind What People Talked About Lidar Robot Navigation 20 Years Ago
LiDAR and Robot Navigation

LiDAR is among the essential capabilities required for mobile robots to navigate safely. It offers a range of functions such as obstacle detection and path planning.

2D lidar scans the environment in a single plane, making it easier and more cost-effective compared to 3D systems. This creates an enhanced system that can identify obstacles even if they're not 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 environment. These sensors calculate distances by sending pulses of light and analyzing the amount of time it takes for each pulse to return. The information is then processed 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 sense of LiDAR gives robots a comprehensive understanding of their surroundings, providing them with the confidence to navigate diverse scenarios. Accurate localization is a major strength, as the technology pinpoints precise positions based on cross-referencing data with maps already in use.

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. But the principle is the same across all models: the sensor transmits an optical pulse that strikes the surrounding environment and returns to the sensor. The process repeats thousands of times per second, resulting in an enormous collection of points that represents the surveyed area.

Each return point is unique, based on the surface object reflecting the pulsed light. Buildings and trees for instance have different reflectance levels than bare earth or water. The intensity of light is dependent on the distance and the scan angle of each pulsed pulse.

The data is then compiled into a complex three-dimensional representation of the area surveyed - called a point cloud which can be viewed through an onboard computer system to aid in navigation. The point cloud can be filterable so that only the area that is desired is displayed.

Or, the point cloud could be rendered in true color by matching the reflection light to the transmitted light. This allows for a better visual interpretation, as well as an improved spatial analysis. The point cloud can also be marked with GPS information, which provides precise time-referencing and temporal synchronization, useful for quality control and time-sensitive analyses.

LiDAR can be used in many different applications and industries. It is utilized on drones to map topography and for forestry, as well on autonomous vehicles that produce an electronic map to ensure safe navigation. It is also utilized to assess the vertical structure in forests which aids researchers in assessing the carbon storage capacity of biomass and carbon sources. best budget lidar robot vacuum include environmental monitoring and detecting changes in atmospheric components, such as CO2 or greenhouse gases.

Range Measurement Sensor


A LiDAR device consists of a range measurement device that emits laser beams repeatedly toward objects and surfaces. The pulse is reflected back and the distance to the object or surface can be determined by determining the time it takes for the pulse to reach the object and return to the sensor (or reverse). The sensor is typically mounted on a rotating platform to ensure that measurements of range are made quickly across a complete 360 degree sweep. These two-dimensional data sets give a detailed image of the robot's surroundings.

There are a variety of range sensors, and they have different minimum and maximum ranges, resolutions and fields of view. KEYENCE offers a wide range 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 operational area. It can be combined with other sensors such as cameras or vision systems to increase the efficiency and durability.

The addition of cameras adds additional visual information that can be used to assist in the interpretation of range data and to improve the accuracy of navigation. Certain vision systems are designed to use range data as an input to a computer generated model of the surrounding environment which can be used to guide the robot according to what it perceives.

To make the most of the LiDAR system it is essential to be aware of how the sensor works and what it is able to do. In most cases, the robot is moving between two crop rows and the aim is to determine the right row by using the LiDAR data set.

A technique called simultaneous localization and mapping (SLAM) can be used to achieve this. SLAM is an iterative algorithm which makes use of an amalgamation of known conditions, such as the robot's current position and orientation, modeled forecasts using its current speed and heading sensor data, estimates of noise and error quantities, and iteratively approximates the solution to determine the robot's position and position. By using this method, the robot will be able to navigate through complex and unstructured environments without the necessity of reflectors or other markers.

SLAM (Simultaneous Localization & Mapping)

The SLAM algorithm plays a key role in a robot's capability to map its surroundings and locate itself within it. Its evolution is a major research area for robotics and artificial intelligence. This paper surveys a variety of the most effective approaches to solve the SLAM problem and discusses the problems that remain.

The main goal of SLAM is to calculate the sequence of movements of a robot in its surroundings and create a 3D model of that environment. The algorithms used in SLAM are based on features extracted from sensor data, which can be either laser or camera data. These features are defined as features or points of interest that are distinguished from other features. They can be as simple as a corner or plane or more complicated, such as shelving units or pieces of equipment.

The majority of Lidar sensors only have limited fields of view, which can limit the data available to SLAM systems. Wide FoVs allow the sensor to capture a greater portion of the surrounding environment, which could result in a more complete mapping of the environment and a more precise navigation system.

To accurately determine the location of the robot, the SLAM must be able to match point clouds (sets in the space of data points) from the current and the previous environment. There are a myriad of algorithms that can be employed to achieve this goal, including iterative closest point and normal distributions transform (NDT) methods. These algorithms can be merged with sensor data to create a 3D map of the surrounding, which can be displayed as an occupancy grid or a 3D point cloud.

A SLAM system is extremely complex and requires substantial processing power in order to function efficiently. This poses problems for robotic systems which must perform in real-time or on a small hardware platform. To overcome these issues, a SLAM system can be optimized to the particular sensor hardware and software environment. For example a laser scanner with large FoV and high resolution could require more processing power than a smaller, lower-resolution scan.

Map Building

A map is an image of the environment that can be used for a number of purposes. It is usually three-dimensional and serves a variety of functions. It can be descriptive, showing the exact location of geographic features, for use in various applications, like an ad-hoc map, or an exploratory one searching for patterns and connections between various phenomena and their properties to discover deeper meaning in a topic, such as many thematic maps.

Local mapping makes use of the data that LiDAR sensors provide on the bottom of the robot slightly above ground level to build a two-dimensional model of the surroundings. This is accomplished by the sensor that provides distance information from the line of sight of each pixel of the rangefinder in two dimensions which permits topological modelling of the surrounding area. The most common navigation and segmentation algorithms are based on this data.

Scan matching is an algorithm that utilizes distance information to estimate the orientation and position of the AMR for each time point. This is accomplished by minimizing the difference between the robot's future state and its current state (position and rotation). Scanning matching can be achieved with a variety of methods. The most well-known is Iterative Closest Point, which has seen numerous changes over the years.

Another method for achieving local map construction is Scan-toScan Matching. This algorithm is employed when an AMR does not have a map, or the map that it does have doesn't correspond to its current surroundings due to changes. This method is extremely susceptible to long-term map drift, as the accumulation of pose and position corrections are subject to inaccurate updates over time.

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

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