11 Ways To Completely Sabotage Your Lidar Robot Navigation LiDAR and Robot Navigation

LiDAR is among the most important capabilities required by mobile robots to navigate safely. It comes with a range of functions, including obstacle detection and route planning.

2D lidar scans the surrounding in one plane, which is simpler and cheaper than 3D systems. This allows for a robust system that can detect objects even if they're not exactly aligned with the sensor plane.

LiDAR Device

LiDAR (Light detection and Ranging) sensors employ eye-safe laser beams to "see" the world around them. By transmitting pulses of light and measuring the time it takes to return each pulse the systems can determine distances between the sensor and the objects within their field of view. This data is then compiled into an intricate 3D representation that is in real-time. the area that is surveyed, referred to as a point cloud.

The precise sensing capabilities of LiDAR gives robots an understanding of their surroundings, empowering them with the ability to navigate diverse scenarios. Accurate localization is an important strength, as LiDAR pinpoints precise locations based on cross-referencing data with existing maps.

Depending on the use, LiDAR devices can vary in terms of frequency as well as range (maximum distance) and resolution. horizontal field of view. The fundamental principle of all LiDAR devices is the same that the sensor sends out the laser pulse, which is absorbed by the environment and returns back to the sensor. This is repeated thousands per second, resulting in an immense collection of points representing the area being surveyed.

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

The data is then processed to create a three-dimensional representation. the point cloud, which can be viewed using an onboard computer to aid in navigation. The point cloud can be reduced to show only the desired area.

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 tagged with GPS data that permits precise time-referencing and temporal synchronization. This is beneficial for quality control and for time-sensitive analysis.

robot vacuum cleaner with lidar is utilized in a wide range of applications and industries. It is used on drones to map topography and for forestry, and on autonomous vehicles which create a digital map for safe navigation. It can also be utilized to assess the vertical structure in forests which aids researchers in assessing the carbon storage capacity of biomass and carbon sources. Other applications include monitoring the environment and monitoring changes in atmospheric components, such as greenhouse gases or CO2.

Range Measurement Sensor

A LiDAR device is a range measurement device that emits laser pulses repeatedly toward objects and surfaces. The laser beam is reflected and the distance can be measured by observing the amount of time it takes for the laser pulse to be able to reach the object's surface and then return to the sensor. The sensor is usually placed on a rotating platform to ensure that measurements of range are made quickly over a full 360 degree sweep. Two-dimensional data sets give a clear perspective of the robot's environment.

There are many different types of range sensors. They have varying minimum and maximal ranges, resolutions, and fields of view. KEYENCE offers a wide range of these sensors and can help you choose the right solution for your needs.

Range data can be used to create contour maps in two dimensions of the operating area. It can be paired with other sensors, such as cameras or vision system to improve the performance and durability.

The addition of cameras can provide additional visual data to aid in the interpretation of range data and improve the accuracy of navigation. Some vision systems use range data to construct a computer-generated model of environment, which can then be used to guide a robot based on its observations.

To make the most of the LiDAR sensor, it's essential to be aware of how the sensor operates and what it can do. The robot is often able to be able to move between two rows of plants and the goal is to determine the right one by using the LiDAR data.

To achieve this, a technique called simultaneous mapping and locatation (SLAM) may be used. SLAM is an iterative method which uses a combination known circumstances, like the robot's current position and direction, modeled predictions on the basis of its current speed and head, as well as sensor data, with estimates of noise and error quantities, and iteratively approximates a result to determine the robot's location and pose. This method allows the robot to navigate in unstructured and complex environments without the need for markers or reflectors.

SLAM (Simultaneous Localization & Mapping)

The SLAM algorithm plays an important role in a robot's capability to map its environment and to locate itself within it. Its evolution is a major research area for artificial intelligence and mobile robots. This paper reviews a range of the most effective approaches to solving the SLAM problems and highlights the remaining problems.

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

The majority of Lidar sensors only have an extremely narrow field of view, which may limit the information available to SLAM systems. A wider field of view allows the sensor to capture an extensive area of the surrounding area. This could lead to more precise navigation and a full mapping of the surrounding area.

To accurately determine the robot's location, an SLAM must match point clouds (sets of data points) from both the present and the previous environment. There are many algorithms that can be utilized for this purpose that include iterative closest point and normal distributions transform (NDT) methods. These algorithms can be combined with sensor data to produce a 3D map, which can then be displayed as an occupancy grid or 3D point cloud.


A SLAM system is complex and requires significant processing power to operate efficiently. This can present problems for robotic systems which must be able to run in real-time or on a tiny hardware platform. To overcome these obstacles, the SLAM system can be optimized for the specific sensor hardware and software environment. For example, a laser scanner with large FoV and a high resolution might require more processing power than a cheaper scan with a lower resolution.

Map Building

A map is a representation of the surrounding environment that can be used for a variety of purposes. It is usually three-dimensional, and serves a variety of functions. It can be descriptive (showing the precise location of geographical features to be used in a variety applications like a street map) as well as exploratory (looking for patterns and connections between phenomena and their properties in order to discover deeper meaning in a specific subject, such as in many thematic maps), or even explanatory (trying to convey details about the process or object, often using visuals, such as graphs or illustrations).

Local mapping creates a 2D map of the environment by using LiDAR sensors placed at the foot of a robot, a bit above the ground level. This is accomplished by the sensor that provides distance information from the line of sight of every one of the two-dimensional rangefinders that allows topological modeling of the surrounding area. This information is used to develop common segmentation and navigation algorithms.

Scan matching is an algorithm that makes use of distance information to calculate an estimate of the position and orientation for the AMR at each point. This is done by minimizing the error of the robot's current state (position and rotation) and its anticipated future state (position and orientation). Scanning matching can be achieved with a variety of methods. Iterative Closest Point is the most popular technique, and has been tweaked many times over the years.

Scan-toScan Matching is another method to create a local map. This incremental algorithm is used when an AMR does not have a map or the map it does have doesn't match its current surroundings due to changes. This technique is highly susceptible to long-term map drift due to the fact that the accumulated position and pose corrections are subject to inaccurate updates over time.

To address this issue, a multi-sensor fusion navigation system is a more reliable approach that utilizes the benefits of a variety of data types and overcomes the weaknesses of each one of them. This kind of navigation system is more tolerant to errors made by the sensors and is able to adapt to dynamic environments.