LiDAR Robot Navigation

LiDAR robots navigate by using the combination of localization and mapping, as well as path planning. This article will introduce the concepts and demonstrate how they work by using an easy example where the robot achieves the desired goal within a row of plants.

LiDAR sensors have modest power demands allowing them to extend the life of a robot's battery and reduce the need for raw data for localization algorithms. This allows for more repetitions of SLAM without overheating GPU.

LiDAR Sensors

The heart of lidar systems is their sensor, which emits pulsed laser light into the environment. These pulses bounce off objects around them at different angles based on their composition. The sensor monitors the time it takes for each pulse to return and then utilizes that information to determine distances. The sensor is typically placed on a rotating platform allowing it to quickly scan the entire surrounding area at high speed (up to 10000 samples per second).

LiDAR sensors can be classified according to whether they're intended for use in the air or on the ground. Airborne lidars are often mounted on helicopters or an UAVs, which are unmanned. (UAV). Terrestrial LiDAR is usually mounted 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 through an array of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are used by LiDAR systems to determine the precise location of the sensor within space and time. This information is then used to create a 3D representation of the surrounding environment.

LiDAR scanners can also detect different kinds of surfaces, which is particularly beneficial when mapping environments with dense vegetation. When a pulse passes a forest canopy, it will typically register multiple returns. The first one is typically attributable to the tops of the trees while the last is attributed with the surface of the ground. If the sensor can record each peak of these pulses as distinct, it is called discrete return LiDAR.

Distinte return scanning can be useful for analysing the structure of surfaces. For instance forests can produce one or two 1st and 2nd returns, with the final big pulse representing the ground. The ability to separate and store these returns in a point-cloud permits detailed models of terrain.

Once a 3D map of the surrounding area has been built, the robot can begin to navigate based on this data. This involves localization and building a path that will get to a navigation "goal." It also involves dynamic obstacle detection. This is the method of identifying new obstacles that aren't present on the original map and updating the path plan accordingly.

SLAM Algorithms

SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to create a map of its environment and then determine where it is in relation to the map. Engineers use the information for a number of tasks, such as path planning and obstacle identification.

To be able to use SLAM your robot has to have a sensor that provides range data (e.g. A computer that has the right software for processing the data as well as a camera or a laser are required. You also need an inertial measurement unit (IMU) to provide basic positional information. The system can track your robot's location accurately in an undefined environment.

The SLAM process is a complex one, and many different back-end solutions are available. Whatever solution you select for the success of SLAM, it requires a constant interaction between the range measurement device and the software that extracts the data and also the vehicle or robot. This is a dynamic procedure with a virtually unlimited variability.

As the robot moves about the area, it adds new scans to its map. The SLAM algorithm analyzes these scans against the previous ones making use of a process known as scan matching. This allows loop closures to be established. The SLAM algorithm adjusts its robot vacuum with lidar and camera's estimated trajectory when a loop closure has been detected.

The fact that the surroundings changes in time is another issue that makes it more difficult for SLAM. If, for instance, your robot is walking along an aisle that is empty at one point, and then encounters a stack of pallets at another point it might have trouble connecting the two points on its map. The handling dynamics are crucial in this case and are a feature of many modern Lidar SLAM algorithms.

SLAM systems are extremely efficient in navigation and 3D scanning despite these challenges. It is particularly beneficial in environments that don't permit the robot to rely on GNSS-based positioning, like an indoor factory floor. It is crucial to keep in mind that even a properly-configured SLAM system could be affected by mistakes. It is vital to be able recognize these issues and comprehend how they affect the SLAM process to fix them.

Mapping

The mapping function creates a map of the robot's environment. This includes the robot vacuum cleaner lidar and its wheels, actuators, and everything else that is within its vision field. The map is used to perform the localization, planning of paths and obstacle detection. This is a field in which 3D Lidars are particularly useful because they can be regarded as an 3D Camera (with a single scanning plane).

Map creation is a time-consuming process but it pays off in the end. The ability to create a complete, coherent map of the robot's surroundings allows it to perform high-precision navigation, as well being able to navigate around obstacles.

As a rule of thumb, the greater resolution the sensor, more precise the map will be. However, not all robots need high-resolution maps. For example floor sweepers may not need the same amount of detail as an industrial robot navigating factories of immense size.

There are many different mapping algorithms that can be used with LiDAR sensors. Cartographer is a popular algorithm that utilizes the two-phase pose graph optimization technique. It corrects for LiDAR robot navigation drift while maintaining an unchanging global map. It is particularly useful when used in conjunction with the odometry.

Another alternative is GraphSLAM, which uses linear equations to model the constraints of a graph. The constraints are represented as an O matrix, and an X-vector. Each vertice in the O matrix is the distance to the X-vector's landmark. A GraphSLAM Update is a series of subtractions and additions to these matrix elements. The end result is that all the O and X Vectors are updated to reflect the latest observations made by the robot.

SLAM+ is another useful mapping algorithm that combines odometry and mapping using an Extended Kalman filter (EKF). The EKF changes 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 position and update the map.

Obstacle Detection

A robot needs to be able to see its surroundings in order to avoid obstacles and reach its final point. It makes use of sensors like digital cameras, infrared scans, laser radar, and sonar to detect the environment. Additionally, it utilizes inertial sensors to measure its speed and position as well as its orientation. These sensors enable it to navigate in a safe manner and avoid collisions.

A key element of this process is the detection of obstacles, which involves the use of an IR range sensor to measure the distance between the robot and obstacles. The sensor can be positioned on the robot, inside the vehicle, or on poles. It is important to remember that the sensor can be affected by many factors, such as rain, wind, and fog. Therefore, it is important to calibrate the sensor before each use.

A crucial step in obstacle detection is identifying static obstacles, which can be accomplished by using the results of the eight-neighbor cell clustering algorithm. This method is not very accurate because of the occlusion caused by the distance between the laser lines and the camera's angular speed. To address this issue multi-frame fusion was employed to improve 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 subsequent navigation operations, such as path planning. The result of this technique is a high-quality image of the surrounding environment that is more reliable than one frame. The method has been tested with other obstacle detection techniques like YOLOv5 VIDAR, YOLOv5, and monocular ranging, in outdoor tests of comparison.

The results of the test showed that the algorithm was able to accurately determine the height and location of an obstacle, in addition to its tilt and rotation. It also had a good performance in identifying the size of obstacles and its color. The algorithm was also durable and steady even when obstacles moved.