Why Lidar Robot Navigation Is The Best Choice For You? LiDAR Robot Navigation

LiDAR robot navigation is a complicated combination of localization, mapping and path planning. This article will introduce the concepts and show how they work by using an example in which the robot is able to reach an objective within the space of a row of plants.

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

LiDAR Sensors

The sensor is at the center of a Lidar system. It releases laser pulses into the surrounding. These pulses bounce off surrounding objects at different angles depending on their composition. The sensor records the time it takes to return each time and uses this information to determine distances. experienced are placed on rotating platforms, which allows them to scan the surrounding area quickly and at high speeds (10000 samples per second).

LiDAR sensors can be classified according to whether they're designed for applications in the air or on land. Airborne lidar systems are typically connected to aircrafts, helicopters or unmanned aerial vehicles (UAVs). Terrestrial LiDAR is usually installed on a robotic platform that is stationary.

To accurately measure distances, the sensor needs to know the exact position of the robot at all times. This information is usually gathered by a combination of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are utilized by LiDAR systems to determine the precise location of the sensor within space and time. The information gathered is used to create a 3D model of the surrounding environment.

LiDAR scanners are also able to identify various 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 register multiple returns. The first one is typically associated with the tops of the trees while the second is associated with the ground's surface. If the sensor captures each pulse as distinct, this is referred to as discrete return LiDAR.

Discrete return scanning can also be helpful in analyzing surface structure. For instance forests can result in an array of 1st and 2nd return pulses, with the final big pulse representing bare ground. The ability to divide these returns and save them as a point cloud makes it possible for the creation of precise terrain models.

Once an 3D model of the environment is constructed, the robot will be equipped to navigate. This process involves localization and making a path that will take it to a specific navigation "goal." It also involves dynamic obstacle detection. This is the process that identifies new obstacles not included in the original map and adjusts the path plan according to the new obstacles.

SLAM Algorithms

SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to build a map of its environment and then determine where it is in relation to the map. Engineers utilize the information for a number of tasks, including planning a path and identifying obstacles.

To utilize SLAM the robot needs to have a sensor that gives range data (e.g. A computer with the appropriate software to process the data and either a camera or laser are required. You also need an inertial measurement unit (IMU) to provide basic information about your position. The result is a system that can accurately determine the location of your robot in a hazy environment.

The SLAM system is complicated and there are many different back-end options. No matter which one you select the most effective SLAM system requires constant interaction between the range measurement device and the software that collects the data and the robot or vehicle itself. It is a dynamic process with a virtually unlimited variability.

As the robot moves around, it adds new scans to its map. The SLAM algorithm compares these scans with prior ones using a process called scan matching. This helps to establish loop closures. The SLAM algorithm adjusts its estimated robot trajectory once loop closures are detected.


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

Despite these issues however, a properly designed SLAM system is extremely efficient for navigation and 3D scanning. It is particularly useful in environments that don't permit the robot to rely on GNSS-based positioning, like an indoor factory floor. However, it is important to remember that even a well-designed SLAM system may have errors. It is crucial to be able to spot these flaws and understand how they impact the SLAM process in order to fix them.

Mapping

The mapping function creates a map of the robot's surroundings which includes the robot, its wheels and actuators as well as everything else within its view. This map is used to perform the localization, planning of paths and obstacle detection. This is a field where 3D Lidars are particularly useful as they can be treated as a 3D Camera (with one scanning plane).

The process of creating maps may take a while however the results pay off. The ability to build a complete, consistent map of the surrounding area allows it to carry out high-precision navigation, as as navigate around obstacles.

As a rule, the greater the resolution of the sensor, the more precise will be the map. However it is not necessary for all robots to have high-resolution maps. For example, a floor sweeper may not need the same amount of detail as a industrial robot that navigates factories of immense size.

There are a variety of mapping algorithms that can be utilized with LiDAR sensors. One of the most popular algorithms is Cartographer which employs the two-phase pose graph optimization technique to correct for drift and maintain a uniform global map. It is especially useful when paired with the odometry information.

GraphSLAM is another option, which utilizes a set of linear equations to represent the constraints in diagrams. The constraints are represented as an O matrix, as well as an vector X. Each vertice in the O matrix contains a distance from the X-vector's landmark. A GraphSLAM Update is a series subtractions and additions to these matrix elements. The result is that all O and X vectors are updated to take into account the latest observations made by the robot.

Another helpful mapping algorithm is SLAM+, which combines the use of odometry with 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 mapped by the sensor. The mapping function can then make use of this information to better estimate its own position, allowing it to update the base map.

Obstacle Detection

A robot needs to be able to detect its surroundings so that it can avoid obstacles and reach its goal. It makes use of sensors such as digital cameras, infrared scanners, sonar and laser radar to sense its surroundings. It also uses inertial sensors to determine its speed, location and its orientation. These sensors help it navigate safely and avoid collisions.

One important part of this process is obstacle detection that involves the use of a range sensor to determine the distance between the robot and obstacles. The sensor can be placed on the robot, in the vehicle, or on poles. It is crucial to keep in mind that the sensor could be affected by a variety of elements like rain, wind and fog. Therefore, it is essential to calibrate the sensor prior to every use.

A crucial step in obstacle detection is the identification of static obstacles. This can be done 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 created by the distance between the different laser lines and the angle of the camera making it difficult to detect static obstacles in a single frame. To address this issue, a technique of multi-frame fusion was developed to increase the accuracy of detection of static obstacles.

The method of combining roadside unit-based and vehicle camera obstacle detection has been proven to increase the efficiency of processing data and reserve redundancy for further navigational tasks, like path planning. This method provides an image of high-quality and reliable of the surrounding. The method has been compared with other obstacle detection methods including YOLOv5 VIDAR, YOLOv5, and monocular ranging in outdoor comparative tests.

The experiment results showed that the algorithm could accurately determine the height and position of obstacles as well as its tilt and rotation. It was also able detect the color and size of the object. The algorithm was also durable and stable even when obstacles moved.