Notes

10 Erroneous Answers To Common Lidar Robot Navigation Questions: Do You Know The Right Answers?
LiDAR and Robot Navigation

LiDAR is among the central capabilities needed for mobile robots to safely navigate. It can perform a variety of functions, such as obstacle detection and route planning.

2D lidar scans the surroundings in a single plane, which is easier and more affordable than 3D systems. This creates a powerful system that can identify objects even if they're not exactly aligned with the sensor plane.

LiDAR Device

LiDAR sensors (Light Detection and Ranging) use laser beams that are safe for eyes to "see" their environment. By sending out light pulses and observing the time it takes for each returned pulse they can calculate distances between the sensor and objects within its field of vision. The data is then compiled into a complex, real-time 3D representation of the area being surveyed. This is known as a point cloud.

The precise sensing prowess of LiDAR provides robots with an knowledge of their surroundings, providing them with the ability to navigate diverse scenarios. Accurate localization is an important strength, as the technology pinpoints precise locations using cross-referencing of data with maps that are already in place.

The LiDAR technology varies based 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 across all models: the sensor emits the laser pulse, which hits the surrounding environment and returns to the sensor. This process is repeated thousands of times per second, creating an immense collection of points representing the surveyed area.

Each return point is unique and is based on the surface of the object that reflects the pulsed light. Buildings and trees, for example, have different reflectance percentages than the bare earth or water. The intensity of light varies depending on the distance between pulses as well as the scan angle.

The data is then processed to create a three-dimensional representation. an image of a point cloud. This can be viewed using an onboard computer for navigational purposes. The point cloud can be filtered so that only the desired area is shown.

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

LiDAR is a tool that can be utilized in a variety of industries and applications. It is utilized on drones to map topography, and for forestry, and on autonomous vehicles which create an electronic map for safe navigation. lidar robot vacuum Robot Vacuum Mops can also be utilized to assess the vertical structure of forests, which helps researchers assess carbon storage capacities and biomass. Other applications include environmental monitors and monitoring changes in atmospheric components like CO2 or greenhouse gasses.

Range Measurement Sensor

The core of a LiDAR device is a range measurement sensor that emits a laser pulse toward surfaces and objects. The laser beam is reflected and the distance can be measured by observing the amount of time it takes for the laser's pulse to reach the object or surface and then return to the sensor. Sensors are placed on rotating platforms that allow rapid 360-degree sweeps. These two-dimensional data sets offer an accurate picture 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 has a variety of sensors available and can help you select the most suitable one for your needs.

Range data is used to create two-dimensional contour maps of the operating area. It can also be combined with other sensor technologies, such as cameras or vision systems to increase the performance and durability of the navigation system.

The addition of cameras adds additional visual information that can be used to assist in the interpretation of range data and improve accuracy in navigation. Certain vision systems are designed to utilize range data as input to an algorithm that generates a model of the environment that can be used to guide the robot by interpreting what it sees.

It's important to understand how a LiDAR sensor works and what it can do. The robot is often able to be able to move between two rows of crops and the objective is to identify the correct one by using the LiDAR data.

A technique called simultaneous localization and mapping (SLAM) can be used to achieve this. SLAM is an iterative method which uses a combination known conditions, such as the robot's current position and direction, modeled predictions on the basis of its current speed and head, as well as sensor data, as well as estimates of noise and error quantities and then iteratively approximates a result to determine the robot's position and location. This technique allows the robot to move in unstructured and complex environments without the use of markers or reflectors.

SLAM (Simultaneous Localization & Mapping)

The SLAM algorithm plays a key role in a robot's ability to map its surroundings and to locate itself within it. The evolution of the algorithm is a key research area for artificial intelligence and mobile robots. This paper reviews a range of current approaches to solve the SLAM issues and discusses the remaining problems.

The main objective of SLAM is to determine the robot's sequential movement in its environment while simultaneously building a 3D map of that environment. The algorithms used in SLAM are based on features that are derived from sensor data, which could be laser or camera data. These features are defined by the objects or points that can be distinguished. They can be as simple as a corner or plane or more complicated, such as an shelving unit or piece of equipment.

Most Lidar sensors have only limited fields of view, which could restrict the amount of data available to SLAM systems. Wide FoVs allow the sensor to capture more of the surrounding environment which can allow for a more complete map of the surrounding area and a more accurate navigation system.

To accurately estimate the robot's location, an SLAM must be able to match point clouds (sets in the space of data points) from both the current and the previous environment. There are a myriad of algorithms that can be used for this purpose such as iterative nearest point and normal distributions transform (NDT) methods. These algorithms can be used in conjunction with sensor data to create an 3D map, which can then 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 operate efficiently. This can present difficulties for robotic systems that have to be able to run in real-time or on a limited hardware platform. To overcome these challenges a SLAM can be optimized to the sensor hardware and software environment. For instance, a laser sensor with an extremely high resolution and a large FoV may require more resources than a lower-cost low-resolution scanner.

Map Building

A map is an image of the world generally in three dimensions, that serves many purposes. It could be descriptive (showing accurate location of geographic features to be used in a variety of applications like a street map) as well as exploratory (looking for patterns and relationships between various phenomena and their characteristics in order to discover deeper meanings in a particular topic, as with many thematic maps) or even explanatory (trying to communicate details about the process or object, often through visualizations such as graphs or illustrations).

Local mapping makes use of the data that LiDAR sensors provide at the bottom of the robot just above ground level to construct an image of the surrounding area. This is accomplished by the sensor providing distance information from the line of sight of every pixel of the rangefinder in two dimensions that allows topological modeling of the surrounding space. This information is used to design typical navigation and segmentation algorithms.

Scan matching is the method that makes use of distance information to calculate an estimate of orientation and position for the AMR at each time point. This is achieved by minimizing the gap between the robot's future state and its current condition (position and rotation). Scanning matching can be accomplished using a variety of techniques. Iterative Closest Point is the most well-known technique, and has been tweaked numerous times throughout the years.

Another method for achieving local map creation is through Scan-to-Scan Matching. This is an incremental algorithm that is employed when the AMR does not have a map, or the map it does have does not closely match its current surroundings due to changes in the surroundings. This approach is very susceptible to long-term drift of the map due to the fact that the cumulative position and pose corrections are subject to inaccurate updates over time.

To address this issue To overcome this problem, a multi-sensor navigation system is a more reliable approach that takes advantage of different types of data and counteracts the weaknesses of each one of them. This type of navigation system is more tolerant to errors made by the sensors and can adjust to dynamic environments.

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