lidar robot navigation (link webpage)

LiDAR robot navigation is a complex combination of mapping, localization and path planning. This article will introduce these concepts and explain how they work together using an easy example of the robot achieving its goal in a row of crops.

LiDAR sensors have low power requirements, which allows them to extend the battery life of a robot and reduce the need for raw data for localization algorithms. This allows for more repetitions of SLAM without overheating the GPU.

LiDAR Sensors

The sensor is the heart of the Lidar system. It releases laser pulses into the environment. These pulses hit surrounding objects and bounce back to the sensor at a variety of angles, depending on the composition of the object. The sensor records the time it takes for each return and then uses it to determine distances. Sensors are mounted on rotating platforms, which allows them to scan the area around them quickly and at high speeds (10000 samples per second).

LiDAR sensors are classified by the type of sensor they are designed for applications in the air or on land. Airborne lidar systems are typically mounted on aircrafts, helicopters or unmanned aerial vehicles (UAVs). Terrestrial LiDAR systems are usually mounted on a stationary robot platform.

To accurately measure distances, the sensor must be able to determine the exact location of the robot. This information is usually captured through a combination of inertial measuring units (IMUs), GPS, and time-keeping electronics. lidar robot vacuum cleaner systems use sensors to calculate the exact location of the sensor in space and time, which is then used to create a 3D map of the environment.

LiDAR scanners are also able to identify various types of surfaces which is especially useful when mapping environments with dense vegetation. For instance, when an incoming pulse is reflected through a forest canopy, it will typically register several returns. Typically, the first return is attributed to the top of the trees while the final return is associated with the ground surface. If the sensor captures each pulse as distinct, it is referred to as discrete return LiDAR.

The use of Discrete Return scanning can be helpful in analyzing surface structure. For instance, a forest area could yield an array of 1st, 2nd and 3rd returns with a final large pulse that represents the 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 constructed the robot will be equipped to navigate. This process involves localization and creating a path to reach a navigation "goal." It also involves dynamic obstacle detection. This process detects new obstacles that are not listed in the map that was created and updates the path plan according to the new obstacles.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its environment and then determine its location relative to that map. Engineers make use of this information for a number of tasks, including planning a path and identifying obstacles.

To enable SLAM to work, your robot must have sensors (e.g. a camera or laser) and a computer with the appropriate software to process the data. Also, you need an inertial measurement unit (IMU) to provide basic positional information. The system can track the precise location of your robot in a hazy environment.

The SLAM system is complex and offers a myriad of back-end options. Whatever option you select for an effective SLAM, it requires constant interaction between the range measurement device and the software that extracts the data and the robot or vehicle. This is a dynamic process with a virtually unlimited variability.

As the robot moves, it adds scans to its map. The SLAM algorithm compares these scans with previous ones by using a process called scan matching. This allows loop closures to be established. If a loop closure is discovered when loop closure is detected, the SLAM algorithm utilizes this information to update its estimate of the robot's trajectory.

Another issue that can hinder SLAM is the fact that the scene changes in time. If, for instance, your robot is walking along an aisle that is empty at one point, and then encounters a stack of pallets at a different location it may have trouble finding the two points on its map. This is where the handling of dynamics becomes crucial and is a common characteristic of modern Lidar SLAM algorithms.

Despite these challenges, a properly configured SLAM system is extremely efficient for LiDAR Robot Navigation navigation and 3D scanning. It is particularly useful in environments where the robot isn't able to rely on GNSS for positioning for example, an indoor factory floor. However, it is important to note that even a properly configured SLAM system may have mistakes. To correct these mistakes, it is important to be able to recognize them and comprehend their impact on the SLAM process.

Mapping

The mapping function creates a map of a robot's surroundings. This includes the robot as well as its wheels, actuators and everything else within its field of vision. This map is used to aid in location, route planning, and obstacle detection. This is an area in which 3D Lidars can be extremely useful as they can be treated as an 3D Camera (with a single scanning plane).

The map building process can take some time however, the end result pays off. The ability to build a complete and consistent map of the robot's surroundings allows it to move with high precision, and also around obstacles.

As a rule of thumb, the greater resolution the sensor, the more accurate the map will be. However it is not necessary for all robots to have high-resolution maps. For example, a floor sweeper may not require the same degree of detail as an industrial robot navigating large factory facilities.

There are a variety of mapping algorithms that can be utilized with LiDAR sensors. Cartographer is a well-known algorithm that employs a two-phase pose graph optimization technique. It adjusts for drift while maintaining an accurate global map. It is particularly beneficial when used in conjunction with odometry data.

GraphSLAM is a different option, which uses a set of linear equations to model the constraints in diagrams. The constraints are modeled as an O matrix and a the X vector, with every vertice of the O matrix containing a distance to a landmark on the X vector. A GraphSLAM Update is a sequence of additions and subtractions on these matrix elements. The end result is that both the O and X vectors are updated to account for the new observations made by the robot vacuums with lidar.

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 position as well as the uncertainty of the features drawn by the sensor. This information can be used by the mapping function to improve its own estimation of its location, and also to update the map.

Obstacle Detection

A robot needs to be able to sense its surroundings to avoid obstacles and get to its desired point. It uses sensors such as digital cameras, infrared scans sonar and laser radar to determine the surrounding. It also makes use of an inertial sensor to measure its speed, location and orientation. These sensors allow it to navigate without danger and avoid collisions.

One important part of this process is the detection of obstacles that consists of the use of sensors to measure the distance between the robot and obstacles. The sensor can be placed on the robot, inside the vehicle, or on poles. It is crucial to remember that the sensor is affected by a variety of factors, including wind, rain and fog. Therefore, it is essential to calibrate the sensor prior to each use.

The most important aspect of obstacle detection is the identification of static obstacles. This can be accomplished by using the results of the eight-neighbor cell clustering algorithm. However this method is not very effective in detecting obstacles due to the occlusion caused by the distance between the different laser lines and the angle of the camera, which makes it difficult to recognize static obstacles in one frame. To overcome this problem multi-frame fusion was implemented to increase the accuracy of the static obstacle detection.

The method of combining roadside unit-based and obstacle detection using a vehicle camera has been proven to improve the efficiency of processing data and reserve redundancy for further navigation operations, such as path planning. This method provides an accurate, high-quality image of the surrounding. In outdoor tests the method was compared to other obstacle detection methods such as YOLOv5 monocular ranging, VIDAR.

The results of the experiment revealed that the algorithm was able accurately determine the location and height of an obstacle, as well as its rotation and tilt. It was also able to identify the color and size of the object. The method was also robust and steady, even when obstacles moved.