LiDAR and Robot Navigation

LiDAR is among the most important capabilities required by mobile robots to navigate safely. It has a variety of functions, such as obstacle detection and route planning.

2D lidar scans the surrounding in one plane, which is simpler and more affordable than 3D systems. This creates a more robust system that can detect obstacles even if they aren't aligned with the sensor plane.

LiDAR Device

LiDAR (Light detection and Ranging) sensors employ eye-safe laser beams to "see" the environment around them. These sensors calculate distances by sending out pulses of light, and then calculating the time taken for each pulse to return. The information is then processed into a complex 3D representation that is in real-time. the surveyed area known as a point cloud.

The precise sensing prowess of LiDAR provides robots with an understanding of their surroundings, providing them with the ability to navigate diverse scenarios. Accurate localization is an important advantage, as LiDAR pinpoints precise locations using cross-referencing of data with existing maps.

LiDAR devices vary depending on their use in terms of frequency (maximum range) and resolution as well as horizontal field of vision. However, the basic principle is the same across all models: the sensor transmits a laser pulse that hits the surrounding environment and returns to the sensor. This process is repeated thousands of times every second, lidar robot Navigation leading to an enormous number of points which represent the area that is surveyed.

Each return point is unique due to the composition of the surface object reflecting the light. For example buildings and trees have different reflective percentages than bare earth or water. Light intensity varies based on the distance and scan angle of each pulsed pulse as well.

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

The point cloud can be rendered in true color by matching the reflected light with the transmitted light. This results in a better visual interpretation, as well as an accurate spatial analysis. The point cloud may also be labeled with GPS information, which provides temporal synchronization and accurate time-referencing which is useful for quality control and time-sensitive analyses.

LiDAR is utilized in a variety of industries and applications. It is used by drones to map topography and for forestry, as well on autonomous vehicles that produce an electronic map for safe navigation. It is also used to determine the vertical structure of forests, which helps researchers to assess the biomass and carbon sequestration capabilities. Other uses include environmental monitors and monitoring changes to atmospheric components like CO2 or greenhouse gasses.

Range Measurement Sensor

The core of lidar robot navigation (Read the Full Content) devices is a range measurement sensor that emits a laser pulse toward objects and surfaces. This pulse is reflected, and the distance can be determined by measuring the time it takes for the laser pulse to reach the object or surface and then return to the sensor. Sensors are mounted on rotating platforms that allow rapid 360-degree sweeps. Two-dimensional data sets provide a detailed perspective of the robot's environment.

There are various types of range sensors and all of them have different ranges for minimum and maximum. They also differ in their resolution and field. KEYENCE offers a wide range of sensors that are available and can help you choose the right one for your application.

Range data is used to generate two dimensional contour maps of the operating area. It can be paired with other sensor technologies, such as cameras or vision systems to enhance the performance and robustness of the navigation system.

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

To make the most of the LiDAR system it is essential to have a thorough understanding of how the sensor works and what it can accomplish. Oftentimes the robot will move between two rows of crop and the aim is to identify the correct row using the LiDAR data set.

To achieve this, a technique known as simultaneous mapping and localization (SLAM) may be used. SLAM is an iterative algorithm that uses the combination of existing circumstances, such as the robot's current position and orientation, modeled predictions based on its current speed and heading sensor data, estimates of error and noise quantities, and iteratively approximates the solution to determine the robot's location and position. By using this method, the robot will be able to navigate through complex and unstructured environments without the need for reflectors or other markers.

SLAM (Simultaneous Localization & Mapping)

The SLAM algorithm is the key to a robot's ability build a map of its environment and localize itself within that map. Its evolution is a major research area in the field of artificial intelligence and mobile robotics. This paper examines a variety of current approaches to solving the SLAM problem and outlines the issues that remain.

The main goal of SLAM is to estimate the robot's movements in its surroundings while simultaneously constructing a 3D model of that environment. The algorithms used in SLAM are based upon features derived from sensor information that could be camera or laser data. These features are identified by points or objects that can be identified. 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 robot vacuums sensors have only a small field of view, which could limit the data available to SLAM systems. A larger field of view permits the sensor to capture a larger area of the surrounding environment. This could lead to an improved navigation accuracy and a more complete map of the surroundings.

To accurately determine the location of the robot, an SLAM must be able to match point clouds (sets in space of data points) from the present and previous environments. This can be done using a number of algorithms such as the iterative nearest point and normal distributions transformation (NDT) methods. These algorithms can be paired with sensor data to create an 3D map that can later be displayed as an occupancy grid or 3D point cloud.

A SLAM system is complex and requires a significant amount of processing power to operate efficiently. This could pose challenges for robotic systems which must achieve real-time performance or run on a limited hardware platform. To overcome these issues, a SLAM can be adapted to the hardware of the sensor and software. For instance a laser scanner with an extremely high resolution and a large FoV may require more resources than a cheaper and lower resolution scanner.

Map Building

A map is a representation of the environment generally in three dimensions, which serves many purposes. It could be descriptive (showing accurate location of geographic features that can be used in a variety of applications like street maps) or exploratory (looking for patterns and relationships between phenomena and their properties in order to discover deeper meanings in a particular subject, like many thematic maps) or even explanatory (trying to convey details about the process or object, typically through visualisations, such as illustrations or graphs).

Local mapping is a two-dimensional map of the environment by using LiDAR sensors that are placed at the base of a robot, a bit above the ground level. This is accomplished through 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 area. This information is used to create normal segmentation and navigation algorithms.

Scan matching is an algorithm that takes advantage of the distance information to calculate an estimate of orientation and position for the AMR for each time point. This is accomplished by minimizing the differences between the robot's anticipated future state and its current condition (position, rotation). Scanning match-ups can be achieved using a variety of techniques. The most popular one is Iterative Closest Point, which has undergone several modifications over the years.

Another method for achieving local map construction is Scan-toScan Matching. This algorithm is employed when an AMR doesn't have a map or the map it does have doesn't match its current surroundings due to changes. This approach is susceptible to a long-term shift in the map since the accumulated corrections to position and pose are susceptible to inaccurate updating over time.

To overcome this problem, a multi-sensor fusion navigation system is a more reliable approach that takes advantage of multiple data types and counteracts the weaknesses of each of them. This type of system is also more resistant to errors in the individual sensors and can deal with environments that are constantly changing.