LiDAR Robot Navigation

LiDAR robot navigation is a complex combination of localization, mapping and path planning. This article will introduce these concepts and show how they function together with a simple example of the robot achieving a goal within a row of crops.

LiDAR sensors are relatively low power requirements, allowing them to prolong the battery life of a robot and decrease the raw data requirement for localization algorithms. This allows for more versions of the SLAM algorithm without overheating the GPU.

LiDAR Sensors

The sensor is at the center of Lidar systems. It emits laser beams into the surrounding. These light pulses strike objects and bounce back to the sensor at various angles, depending on the composition of the object. The sensor records the amount of time required for each return, which is then used to determine distances. Sensors are placed on rotating platforms that 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 applications in the air or on land. Airborne lidars are often mounted on helicopters or an unmanned aerial vehicle (UAV). Terrestrial LiDAR systems are usually mounted on a stationary robot platform.

To accurately measure distances, the sensor must be aware of the exact location of the robot at all times. This information is typically captured through an array of inertial measurement units (IMUs), GPS, and time-keeping electronics. LiDAR systems make use of sensors to compute the precise location of the sensor in space and time, which is then used to build up a 3D map of the surroundings.

LiDAR scanners are also able to identify different types of surfaces, which is particularly useful when mapping environments with dense vegetation. For instance, LiDAR robot navigation if an incoming pulse is reflected 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 one is attributed to the surface of the ground. If the sensor records each peak of these pulses as distinct, it is called discrete return LiDAR.

The Discrete Return scans can be used to study the structure of surfaces. For instance, a forest region could produce a sequence of 1st, 2nd and 3rd returns with a final, large pulse representing the bare ground. The ability to separate and store these returns as a point-cloud allows for precise models of terrain.

Once a 3D map of the surroundings is created, the robot vacuums with lidar can begin to navigate using this information. This process involves localization, building a path to reach a goal for navigation and dynamic obstacle detection. This is the process of identifying new obstacles that are not present in the original map, and then updating the plan accordingly.

SLAM Algorithms

SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to construct an outline of its surroundings and then determine where it is in relation to the map. Engineers make use of this information to perform a variety of tasks, such as planning routes and obstacle detection.

To allow SLAM to work it requires sensors (e.g. a camera or laser), and a computer with the appropriate software to process the data. Also, you will require an IMU to provide basic information about your position. The system can track your robot's exact location in an undefined environment.

The SLAM process is complex and many back-end solutions exist. Whatever solution you select for your SLAM system, a successful SLAM system requires a constant interaction between the range measurement device, the software that extracts the data and the vehicle or robot. This is a highly dynamic procedure that has an almost unlimited amount of variation.

As the robot moves about, it adds new scans to its map. The SLAM algorithm then compares these scans to previous ones using a process known as scan matching. This aids in establishing loop closures. The SLAM algorithm updates its estimated robot trajectory once loop closures are discovered.

Another factor that complicates SLAM is the fact that the scene changes as time passes. For instance, if your robot is walking along an aisle that is empty at one point, and then comes across a pile of pallets at a different point, it may have difficulty finding the two points on its map. The handling dynamics are crucial in this situation, and they are a part of a lot of modern Lidar SLAM algorithms.

SLAM systems are extremely effective in 3D scanning and navigation despite the challenges. It is particularly useful in environments where the robot can't depend on GNSS to determine its position for positioning, like an indoor factory floor. It is important to note that even a properly configured SLAM system can experience errors. It is crucial to be able to detect these flaws and understand how they impact the SLAM process to correct them.

Mapping

The mapping function creates a map of the robot's environment. This includes the robot as well as its wheels, actuators and everything else that falls within its field of vision. This map is used for the localization of the robot, route planning and obstacle detection. This is an area in which 3D lidars can be extremely useful, as they can be used like the equivalent of a 3D camera (with only one scan plane).

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

In general, the higher the resolution of the sensor then the more accurate will be the map. Not all robots require high-resolution maps. For instance a floor-sweeping robot may not require the same level of detail as an industrial robotic system that is navigating factories of a large size.

There are many different mapping algorithms that can be utilized with LiDAR sensors. Cartographer is a very popular algorithm that employs a two-phase pose graph optimization technique. It corrects for drift while ensuring an unchanging global map. It is particularly useful when combined with Odometry.

Another option is GraphSLAM that employs linear equations to model the constraints in a graph. The constraints are modelled as an O matrix and an one-dimensional X vector, each vertex of the O matrix representing the distance to a point on the X vector. A GraphSLAM update consists of the addition and subtraction operations on these matrix elements which means that all of the X and O vectors are updated to account for new information about the robot.

SLAM+ is another useful mapping algorithm that combines odometry and mapping using an Extended Kalman filter (EKF). The EKF updates not only the uncertainty in the robot's current location, but also the uncertainty of the features that were 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 must be able to perceive its surroundings to avoid obstacles and reach its goal point. It uses sensors such as digital cameras, infrared scans sonar, laser radar and others to detect the environment. It also makes use of an inertial sensors to monitor its speed, position and orientation. These sensors assist it in navigating in a safe way and prevent collisions.

A range sensor is used to measure the distance between the robot and the obstacle. The sensor can be mounted to the vehicle, the robot or even a pole. It is crucial to keep in mind that the sensor can be affected by a variety of elements, including rain, wind, or fog. It is crucial to calibrate the sensors before each use.

A crucial step in obstacle detection is to identify static obstacles. This can be accomplished by using the results of the eight-neighbor cell clustering algorithm. This method is not very accurate because of the occlusion induced by the distance between laser lines and the camera's angular speed. To solve this issue, a method called multi-frame fusion has been used to increase the accuracy of detection of static obstacles.

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

The results of the experiment revealed that the algorithm was able to accurately determine the height and position of an obstacle, as well as its tilt and rotation. It also had a great performance in identifying the size of obstacles and its color. The algorithm was also durable and stable even when obstacles moved.