Ten Ways To Build Your Lidar Robot Navigation Empire

LiDAR Robot Navigation

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

LiDAR sensors are relatively low power demands allowing them to increase a robot's battery life and decrease the raw data requirement for localization algorithms. This allows for more iterations of SLAM without overheating the GPU.

LiDAR Sensors

The sensor is at the center of a Lidar system. It releases laser pulses into the environment. These light pulses bounce off the surrounding objects in different angles, based on their composition. The sensor measures the amount of time required to return each time and uses this information to determine distances. Sensors are mounted on rotating platforms, which allows them to scan the surroundings quickly and at high speeds (10000 samples per second).

LiDAR sensors are classified based on whether they are designed for applications on land or in the air. Airborne lidars are usually connected to helicopters or an unmanned aerial vehicle (UAV). Terrestrial LiDAR is typically installed on a stationary robot platform.

To accurately measure distances, the sensor must always know the exact location of the robot. This information is gathered by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems utilize sensors to calculate 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 can also be used to recognize different types of surfaces, which is particularly useful when mapping environments that have dense vegetation. When a pulse crosses a forest canopy it will usually produce multiple returns. The first return is usually attributed to the tops of the trees, while the last is attributed with the surface of the ground. If the sensor captures these pulses separately this is known as discrete-return LiDAR.

Distinte return scans can be used to analyze surface structure. For example forests can yield one or two 1st and 2nd returns, with the last one representing the ground. The ability to separate and store these returns as a point cloud permits detailed terrain models.

Once an 3D map of the environment is created and the robot is able to navigate using this data. This process involves localization, creating the path needed to reach a navigation 'goal,' and dynamic obstacle detection. This process identifies new obstacles not included in the map's original version and updates the path plan accordingly.

SLAM Algorithms

SLAM (simultaneous localization and Cheapest mapping) is an algorithm that allows your robot to build a map of its environment and then determine where it is relative to the map. Engineers use the data for a variety of tasks, including the planning of routes and obstacle detection.

To enable SLAM to work it requires sensors (e.g. laser or cheapest camera), and a computer that has the right software to process the data. Also, you will require an IMU to provide basic positioning information. The result is a system that will precisely track the position of your robot in an unknown environment.

The SLAM process is complex and a variety of back-end solutions exist. 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. This is a highly dynamic procedure that has an almost endless amount of variance.

As the robot moves and around, it adds new scans to its map. The SLAM algorithm will then compare these scans to earlier ones using a process called scan matching. This assists in establishing loop closures. When a loop closure has been detected it is then the SLAM algorithm utilizes this information to update its estimated robot trajectory.

Another factor that makes SLAM is the fact that the scene changes in time. If, for instance, your robot is navigating an aisle that is empty at one point, and it comes across a stack of pallets at another point, it may have difficulty finding the two points on its map. Handling dynamics are important in this situation, and they are a part of a lot of modern Lidar SLAM algorithm.

SLAM systems are extremely effective in navigation and 3D scanning despite the challenges. It is particularly useful in environments that do not let the robot rely on GNSS position, such as an indoor factory floor. It is important to remember that even a properly configured SLAM system can be prone to errors. To fix these issues, it is important to be able to spot the effects of these errors and their implications on the SLAM process.

Mapping

The mapping function builds a map of the robot's environment, which includes the robot itself, its wheels and actuators and everything else that is in its field of view. This map is used for the localization of the robot, route planning and obstacle detection. This is a field in which 3D Lidars can be extremely useful as they can be treated as an 3D Camera (with one scanning plane).

The process of building maps may take a while, but the results pay off. The ability to build a complete and coherent map of a robot's environment allows it to navigate with great precision, and also around obstacles.

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

For this reason, there are many different mapping algorithms to use with LiDAR sensors. Cartographer is a well-known algorithm that uses a two phase pose graph optimization technique. It corrects for drift while maintaining a consistent global map. It is particularly beneficial when used in conjunction with the odometry information.

Another alternative is GraphSLAM, which uses linear equations to model the constraints of graph. The constraints are represented as an O matrix and a X vector, with each vertex of the O matrix containing the distance to a point on the X vector. A GraphSLAM update is 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 reflect new observations of the robot.

Another useful mapping algorithm is SLAM+, which combines odometry and mapping using an Extended Kalman filter (EKF). The EKF updates not only the uncertainty in the robot vacuum with lidar and camera's current location, but also the uncertainty in the features that have been mapped by the sensor. This information can be utilized 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 so it can avoid obstacles and reach its final point. It uses sensors like digital cameras, infrared scanners laser radar and sonar to detect its environment. Additionally, it employs inertial sensors that measure its speed and position as well as its orientation. These sensors aid in navigation in a safe and secure manner and avoid collisions.

One of the most important aspects of this process is obstacle detection that consists of the use of sensors to measure the distance between the robot and obstacles. The sensor can be positioned on the robot, inside an automobile or on a pole. It is important to remember that the sensor may be affected by many elements, including rain, wind, and fog. Therefore, it is crucial to calibrate the sensor prior to every use.

An important step in obstacle detection is to identify static obstacles. This can be accomplished using the results of the eight-neighbor cell clustering algorithm. This method isn't very accurate because of the occlusion induced by the distance between the laser lines and the camera's angular velocity. To address this issue, a method of multi-frame fusion has been employed 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. The result of this technique is a high-quality picture of the surrounding area that is more reliable than a single frame. The method has been compared against other obstacle detection methods including YOLOv5 VIDAR, YOLOv5, and monocular ranging, in outdoor tests of comparison.

The results of the experiment proved that the algorithm was able to correctly identify the location and height of an obstacle, in addition to its rotation and tilt. It also had a good performance in identifying the size of obstacles and its color. The method also showed excellent stability and durability even in the presence of moving obstacles.