LiDAR and Robot Navigation

LiDAR is among the essential capabilities required for mobile robots to safely navigate. It provides a variety of capabilities, including obstacle detection and path planning.

2D lidar scans an environment in a single plane, making it easier and more efficient than 3D systems. This creates an improved system that can detect obstacles even if they aren't aligned exactly with the sensor plane.

LiDAR Device

LiDAR (Light detection and Ranging) sensors make use of eye-safe laser beams to "see" the world around them. These systems calculate distances by sending pulses of light, and then calculating the amount of time it takes for each pulse to return. The data is then compiled into an intricate, real-time 3D representation of the area that is surveyed, referred to as a point cloud.

LiDAR's precise sensing capability gives robots an in-depth understanding of their surroundings, giving them the confidence to navigate different situations. LiDAR is particularly effective in pinpointing precise locations by comparing data with existing maps.

Depending on the application the LiDAR device can differ in terms of frequency as well as range (maximum distance), resolution, and horizontal field of view. The basic principle of all LiDAR devices is the same that the sensor emits the laser pulse, which is absorbed by the surrounding area and then returns to the sensor. This is repeated thousands per second, resulting in a huge collection of points that represents the area being surveyed.

Each return point is unique, based on the composition of the surface object reflecting the light. For example trees and buildings have different reflective percentages than bare earth or water. The intensity of light depends on the distance between pulses and the scan angle.

The data is then compiled into a detailed 3-D representation of the surveyed area known as a point cloud - that can be viewed by a computer onboard to aid in navigation. The point cloud can be reduced to display only the desired area.

Or, the point cloud can be rendered in a true color by matching the reflection of light to the transmitted light. This allows for better visual interpretation and more precise analysis of spatial space. The point cloud can be marked with GPS data that can be used to ensure accurate time-referencing and temporal synchronization. This is helpful for quality control, and time-sensitive analysis.

LiDAR is utilized in a myriad of applications and industries. It can be found on drones that are used for topographic mapping and forest work, and on Autonomous cleaning robots vehicles that create a digital map of their surroundings to ensure safe navigation. It is also used to determine the vertical structure in forests which aids researchers in assessing carbon storage capacities and biomass. Other uses include environmental monitoring and detecting changes in atmospheric components, such as greenhouse gases or CO2.

Range Measurement Sensor

The heart of LiDAR devices is a range measurement sensor that emits a laser signal towards objects and surfaces. The laser pulse is reflected, and the distance to the surface or object can be determined by measuring the time it takes the laser pulse to be able to reach the object before returning to the sensor (or vice versa). Sensors are placed on rotating platforms that allow rapid 360-degree sweeps. These two-dimensional data sets give an accurate image of the robot's surroundings.

There are various kinds of range sensor, and they all have different ranges of minimum and maximum. They also differ in their resolution and field. KEYENCE has a variety of sensors and can help you choose the most suitable one for your requirements.

Range data can be used to create contour maps within two dimensions of the operating area. It can be combined with other sensor technologies like cameras or vision systems to enhance the performance and durability of the navigation system.

Cameras can provide additional information in visual terms to assist in the interpretation of range data and improve navigational accuracy. Some vision systems use range data to build a computer-generated model of the environment. This model can be used to guide the robot based on its observations.

To make the most of the LiDAR sensor, it's essential to have a thorough understanding of how the sensor operates and what it is able to do. Oftentimes the robot will move between two rows of crop and the aim is to determine the right row using the LiDAR data set.

A technique called simultaneous localization and mapping (SLAM) can be used to accomplish this. SLAM is an iterative algorithm which uses a combination known conditions such as the robot’s current position and direction, modeled predictions that are based on the current speed and head speed, as well as other sensor data, with estimates of error and noise quantities, and iteratively approximates a result to determine the robot’s location and its pose. This technique lets the robot vacuums with obstacle avoidance lidar move 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 vacuum cleaner lidar's capability to map its surroundings and locate itself within it. Its development is a major research area for robots with artificial intelligence and mobile. This paper reviews a range of current approaches to solving the SLAM problem and outlines the problems that remain.

The primary goal of SLAM is to determine the robot's movements within its environment, while creating a 3D map of that environment. The algorithms used in SLAM are based on features taken from sensor data which can be either laser or camera data. These features are defined as points of interest that can be distinct from other objects. They could be as simple as a corner or a plane or even more complex, for instance, an shelving unit or piece of equipment.

The majority of Lidar sensors have an extremely narrow field of view, which may limit the data that is available to SLAM systems. A wide FoV allows for the sensor to capture more of the surrounding environment which could result in an accurate map of the surrounding area and a more accurate navigation system.

To accurately determine the location of the robot vacuum cleaner with lidar, the SLAM must be able to match point clouds (sets in space of data points) from both the present and the previous environment. There are a myriad of algorithms that can be used for this purpose, including iterative closest point and normal distributions transform (NDT) methods. These algorithms can be paired with sensor data to produce 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 require a significant amount of processing power in order to function efficiently. This can be a challenge for robotic systems that have to achieve real-time performance, or run on a limited hardware platform. To overcome these obstacles, a SLAM system can be optimized to the particular sensor hardware and software environment. For instance a laser sensor with a high resolution and wide FoV may require more resources than a cheaper low-resolution scanner.

Map Building

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

Local mapping utilizes the information generated by LiDAR sensors placed at the base of the vacuum robot with lidar just above ground level to construct a two-dimensional model of the surrounding. To do this, the sensor provides distance information derived from a line of sight from each pixel in the range finder in two dimensions, which allows for topological modeling of the surrounding space. Typical segmentation and navigation algorithms are based on this data.

Scan matching is the algorithm that takes advantage of the distance information to calculate an estimate of orientation and position for the AMR at each time point. This is done by minimizing the error of the robot's current condition (position and rotation) and its anticipated future state (position and orientation). A variety of techniques have been proposed to achieve scan matching. Iterative Closest Point is the most popular, and has been modified many times over the years.

Another way to achieve local map creation is through Scan-to-Scan Matching. This algorithm is employed when an AMR does not have a map, or the map it does have doesn't coincide with its surroundings due to changes. This technique is highly susceptible to long-term map drift due to the fact that the accumulation of pose and position corrections are susceptible to inaccurate updates over time.

To overcome this problem To overcome this problem, a multi-sensor navigation system is a more reliable approach that takes advantage of different types of data and overcomes the weaknesses of each one of them. This type of system is also more resilient to the flaws in individual sensors and can cope with environments that are constantly changing.