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LiDAR Robot Navigation

LiDAR robot navigation is a complicated combination of localization, mapping and path planning. This article will present these concepts and explain how they work together using an example of a robot achieving a goal within a row of crops.

LiDAR sensors are low-power devices that extend the battery life of robots and decrease the amount of raw data needed to run localization algorithms. This allows for more iterations of SLAM without overheating the GPU.

LiDAR Sensors

The core of lidar product systems is their sensor that emits laser light pulses into the environment. The light waves bounce off the surrounding objects at different angles based on their composition. The sensor monitors the time it takes for each pulse to return and uses that information to calculate distances. The sensor is usually placed on a rotating platform which allows it to scan the entire surrounding area at high speed (up to 10000 samples per second).

LiDAR sensors are classified based on the type of sensor they are designed for airborne or terrestrial application. Airborne lidars are typically connected to helicopters or an unmanned aerial vehicle (UAV). Terrestrial LiDAR systems are typically mounted on a static robot platform.

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

lidar robot vacuum cleaner scanners are also able to identify different kinds of surfaces, which is particularly useful when mapping environments with dense vegetation. For example, when an incoming pulse is reflected through a forest canopy, it is likely to register multiple returns. Usually, the first return is attributed to the top of the trees, while the final return is related to the ground surface. If the sensor captures these pulses separately this is known as discrete-return LiDAR.

The Discrete Return scans can be used to analyze the structure of surfaces. For example the forest may result in a series of 1st and 2nd returns with the final big pulse representing the ground. The ability to separate and record these returns in a point-cloud allows for precise models of terrain.

Once a 3D model of the surroundings has been built and the robot is able to navigate using this data. This process involves localization, constructing an appropriate path to reach a goal for navigation and dynamic obstacle detection. This is the process of identifying obstacles that aren't present in the original map, and then updating the plan accordingly.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its surroundings and then determine its location relative to that map. Engineers make use of this information for a variety of tasks, such as planning routes and obstacle detection.

To be able to use SLAM your robot has to be equipped with a sensor that can provide range data (e.g. laser or camera), and a computer that has the appropriate software to process the data. You also need an inertial measurement unit (IMU) to provide basic information about your position. The system will be able to track your robot's location accurately in an unknown environment.

The SLAM process is a complex one and many back-end solutions are available. Whatever solution you select, a successful SLAM system requires a constant interplay between the range measurement device, the software that extracts the data and the vehicle or robot. This is a highly dynamic process that has an almost infinite amount of variability.

As the robot moves it adds scans to its map. The SLAM algorithm analyzes these scans against prior ones making use of a process known as scan matching. This helps to establish loop closures. When a loop closure is detected it is then the SLAM algorithm utilizes this information to update its estimate of the robot's trajectory.

Another factor that complicates SLAM is the fact that the scene changes over time. For example, if your robot walks through an empty aisle at one point, and then encounters stacks of pallets at the next point, it will have difficulty connecting these two points in its map. This is where handling dynamics becomes important and is a common feature of modern Lidar SLAM algorithms.

Despite these difficulties, a properly configured SLAM system is incredibly effective for navigation and 3D scanning. It is especially useful in environments that don't rely on GNSS for its positioning, such as an indoor factory floor. However, it is important to keep in mind that even a well-configured SLAM system can experience errors. To correct these mistakes it is crucial to be able to spot 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 and its wheels, actuators, and everything else within its vision field. The map is used for the localization of the robot, route planning and obstacle detection. This is an area where 3D lidars are extremely helpful since they can be utilized like the equivalent of a 3D camera (with one scan plane).

The process of building maps may take a while, but the results pay off. The ability to create a complete, coherent map of the surrounding area allows it to conduct high-precision navigation, as well being able to navigate around obstacles.

As a general rule of thumb, the higher resolution the sensor, the more accurate the map will be. However there are exceptions to the requirement for high-resolution maps: for example floor sweepers might not require the same degree of detail as an industrial robot vacuum with lidar and camera navigating factories of immense size.

There are a variety of mapping algorithms that can be used with LiDAR sensors. Cartographer is a well-known algorithm that uses the two-phase pose graph optimization technique. It corrects for drift while ensuring a consistent global map. It is especially beneficial when used in conjunction with Odometry data.

GraphSLAM is a different option, which uses a set of linear equations to represent the constraints in diagrams. The constraints are represented by an O matrix, as well as an vector X. Each vertice in the O matrix is an approximate distance from the X-vector's landmark. A GraphSLAM update is an array of additions and subtraction operations on these matrix elements which means that all of the O and X 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 position but also the uncertainty of the features that were drawn by the sensor. The mapping function can then make use of this information to improve its own location, allowing it to update the underlying 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 such as digital cameras, infrared scans, sonar and laser radar to detect the environment. In addition, it uses inertial sensors that measure its speed and position as well as its orientation. These sensors aid in navigation in a safe way and avoid collisions.

One of the most important aspects 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 mounted to the robot, a vehicle or a pole. It is important to remember that the sensor can be affected by a myriad of factors, including wind, rain and fog. Therefore, it is important to calibrate the sensor before every use.

The results of the eight neighbor cell clustering algorithm can be used to detect static obstacles. However, this method is not very effective in detecting obstacles due to the occlusion created by the gap between the laser lines and the angular velocity of the camera making it difficult to recognize static obstacles in a single frame. To solve this issue, a method of multi-frame fusion has been employed to increase the detection accuracy of static obstacles.

The method of combining roadside unit-based and vehicle camera obstacle detection has been proven to improve the efficiency of processing data and reserve redundancy for future navigational operations, like path planning. This method provides an accurate, high-quality image of the surrounding. The method has been compared with other obstacle detection methods like YOLOv5, VIDAR, and monocular ranging, in outdoor comparison experiments.

The experiment results showed that the algorithm could accurately identify the height and position of obstacles as well as its tilt and rotation. It was also able identify the size and color of an object. The method was also robust and reliable, even when obstacles moved.