Why Lidar Robot Navigation Is So Helpful In COVID-19 LiDAR Robot Navigation

LiDAR robot navigation is a complicated combination of mapping, localization and path planning. This article will introduce the concepts and demonstrate how they work by using an example in which the robot achieves a goal within a plant row.

LiDAR sensors are low-power devices that can prolong the life of batteries on robots and reduce the amount of raw data required to run localization algorithms. This allows for a greater number of iterations of SLAM without overheating GPU.

LiDAR Sensors

The sensor is at the center of the Lidar system. It emits laser beams into the environment. These pulses hit surrounding objects and bounce back to the sensor at various angles, based on the structure of the object. The sensor is able to measure the time it takes for each return, which is then used to determine distances. Sensors are placed on rotating platforms, which allow them to scan the surroundings quickly and at high speeds (10000 samples per second).

LiDAR sensors can be classified based on the type of sensor they're designed for, whether use in the air or on the ground. Airborne lidars are usually attached to helicopters or unmanned aerial vehicle (UAV). Terrestrial LiDAR systems are generally mounted on a static robot platform.

To accurately measure distances, the sensor must be able to determine the exact location of the robot. This information is recorded by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are utilized by LiDAR systems to calculate the exact location of the sensor in space and time. This information is used to create a 3D representation of the surrounding environment.

LiDAR scanners can also be used to recognize different types of surfaces, which is particularly useful when mapping environments that have dense vegetation. When a pulse passes through a forest canopy, it is likely to produce multiple returns. The first one is typically associated with the tops of the trees, while the last is attributed with the ground's surface. If the sensor records these pulses separately and is referred to as discrete-return LiDAR.

Discrete return scans can be used to determine the structure of surfaces. For instance, a forest region could produce an array of 1st, 2nd, and 3rd returns, with a last large pulse representing the bare ground. The ability to separate and record these returns as a point-cloud permits detailed models of terrain.

Once a 3D model of the environment is built the robot will be equipped to navigate. This involves localization as well as building a path that will take it to a specific navigation "goal." It also involves dynamic obstacle detection. This is the process of identifying new obstacles that are not present in the original map, and then updating the plan in line with the new obstacles.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm which allows your robot to map its environment and then identify its location in relation to the map. Engineers use this information for a range of tasks, including planning routes and obstacle detection.

To utilize SLAM the robot needs to be equipped with a sensor that can provide range data (e.g. laser or camera), and a computer that has the appropriate software to process the data. You will also need an IMU to provide basic information about your position. The result is a system that will accurately determine the location of your robot in a hazy environment.

The SLAM system is complicated and there are a variety of back-end options. Regardless of which solution you select the most effective SLAM system requires a constant interaction between the range measurement device and the software that extracts the data, and the vehicle or robot itself. This is a dynamic process that is almost indestructible.


As the robot moves it adds scans to its map. robot vacuum cleaner lidar compares these scans with the previous ones using a process known as scan matching. This allows loop closures to be identified. The SLAM algorithm is updated with its robot's estimated trajectory when loop closures are discovered.

Another factor that complicates SLAM is the fact that the environment changes in time. For example, if your robot travels through an empty aisle at one point, and then comes across pallets at the next location it will have a difficult time connecting these two points in its map. The handling dynamics are crucial in this situation, and they are a feature of many modern Lidar SLAM algorithm.

SLAM systems are extremely effective in 3D scanning and navigation despite these limitations. It is particularly useful in environments where the robot can't rely on GNSS for its positioning for example, an indoor factory floor. However, it is important to note that even a well-designed SLAM system can be prone to mistakes. It is essential to be able to spot these issues and comprehend how they impact the SLAM process to correct them.

Mapping

The mapping function creates a map for a robot's environment. This includes the robot, its wheels, actuators and everything else within its field of vision. This map is used for localization, route planning and obstacle detection. This is an area in which 3D Lidars are especially helpful because they can be used as a 3D Camera (with a single scanning plane).

Map creation can be a lengthy process however, it is worth it in the end. The ability to create a complete and consistent map of the environment around a robot allows it to move with high precision, and also around obstacles.

As a general rule of thumb, the greater resolution the sensor, more precise the map will be. However there are exceptions to the requirement for high-resolution maps: for example, a floor sweeper may not require the same degree of detail as a industrial robot that navigates large factory facilities.

There are many different mapping algorithms that can be utilized with LiDAR sensors. One of the most well-known algorithms is Cartographer which employs a two-phase pose graph optimization technique to correct for drift and create an accurate global map. It is particularly useful when combined with Odometry.

Another option is GraphSLAM that employs linear equations to represent the constraints in graph. The constraints are represented as an O matrix and an X vector, with each vertice of the O matrix containing the distance to a point on the X vector. A GraphSLAM update consists of a series of additions and subtraction operations on these matrix elements and the result is that all of the O and X vectors are updated to account for new robot observations.

SLAM+ is another useful mapping algorithm that combines odometry and mapping using an Extended Kalman filter (EKF). The EKF updates the uncertainty of the robot's location as well as the uncertainty of the features recorded by the sensor. This information can be utilized by the mapping function to improve its own estimation of its location and to update the map.

Obstacle Detection

A robot needs to be able to sense its surroundings in order to avoid obstacles and get to its desired point. It employs sensors such as digital cameras, infrared scans, laser radar, and sonar to sense the surroundings. It also makes use of an inertial sensors to monitor its speed, location and its orientation. These sensors help it navigate in a safe manner and avoid collisions.

A range sensor is used to gauge the distance between an obstacle and a robot. The sensor can be positioned on the robot, inside a vehicle or on a pole. It is important to keep in mind that the sensor can be affected by various elements, including rain, wind, or fog. It is essential to calibrate the sensors prior to each use.

The most important aspect of obstacle detection is the identification of static obstacles. This can be done by using the results of the eight-neighbor-cell clustering algorithm. This method is not very precise due to the occlusion induced by the distance between laser lines and the camera's angular speed. To address this issue, multi-frame fusion was used to increase the effectiveness of static obstacle detection.

The method of combining roadside unit-based as well as vehicle camera obstacle detection has been proven to increase the efficiency of data processing and reserve redundancy for subsequent navigational tasks, like path planning. The result of this technique is a high-quality image of the surrounding area that is more reliable than a single frame. In outdoor comparison experiments the method was compared with other methods of obstacle detection such as YOLOv5 monocular ranging, and VIDAR.

The experiment results showed that the algorithm could accurately determine the height and location of an obstacle as well as its tilt and rotation. It also showed a high performance in detecting the size of an obstacle and its color. The method also showed solid stability and reliability even when faced with moving obstacles.