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10 Healthy Lidar Robot Navigation Habits
LiDAR Robot Navigation

LiDAR robots navigate by using a combination of localization, mapping, and also path planning. This article will introduce these concepts and explain how they interact using a simple example of the robot reaching a goal in a row of crop.

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

LiDAR Sensors

The central component of lidar systems is their sensor which emits pulsed laser light into the surrounding. These pulses hit surrounding objects and bounce back to the sensor at various angles, depending on the composition of the object. The sensor measures the time it takes to return each time, which is then used to calculate distances. The sensor is typically placed on a rotating platform, permitting it to scan the entire surrounding area at high speed (up to 10000 samples per second).

LiDAR sensors are classified based on whether they're intended for applications in the air or on land. Airborne lidar systems are usually attached to helicopters, aircraft, or UAVs. (UAVs). Terrestrial LiDAR systems are typically mounted on a static robot platform.

To accurately measure distances, the sensor must be aware of the exact location of the robot at all times. This information is recorded by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are employed by LiDAR systems to calculate the precise location of the sensor in space and time. This information is used to create a 3D model of the surrounding environment.

LiDAR scanners are also able to identify various types of surfaces which is especially beneficial when mapping environments with dense vegetation. For example, when an incoming pulse is reflected through a canopy of trees, it is likely to register multiple returns. The first return is usually attributed to the tops of the trees, while the second one is attributed to the surface of the ground. If the sensor records these pulses in a separate way and is referred to as discrete-return LiDAR.

Distinte return scans can be used to analyze the structure of surfaces. For instance the forest may produce one or two 1st and 2nd return pulses, with the last one representing bare ground. The ability to divide these returns and save them as a point cloud allows for the creation of precise terrain models.

Once a 3D map of the surroundings has been created, the robot can begin to navigate using this information. This process involves localization, creating a path to get to a destination and dynamic obstacle detection. The latter is the process of identifying obstacles that aren't visible on the original map and updating the path plan accordingly.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm which allows your robot to map its surroundings, and then identify its location in relation to the map. Engineers use the information to perform a variety of tasks, including planning a path and identifying obstacles.

To utilize SLAM, your robot needs to be equipped with a sensor that can provide range data (e.g. A computer with the appropriate software for processing the data, as well as cameras or lasers are required. Also, you need an inertial measurement unit (IMU) to provide basic positional information. The system can track your robot's exact location in an undefined environment.

The SLAM process is extremely complex and many back-end solutions are available. Whatever solution you choose to implement an effective SLAM is that it requires constant communication between the range measurement device and the software that extracts data and the robot or vehicle. This is a highly dynamic process that has an almost endless amount of variance.

As the robot moves, it adds new scans to its map. The SLAM algorithm will then compare these scans to earlier ones using a process known as scan matching. This aids in establishing loop closures. The SLAM algorithm updates its estimated robot trajectory once the loop has been closed detected.

The fact that the surrounding can change over time is a further factor that complicates SLAM. For lidar robot , if your robot travels through an empty aisle at one point, and then encounters stacks of pallets at the next spot it will have a difficult time connecting these two points in its map. This is where handling dynamics becomes important and is a common characteristic of the modern Lidar SLAM algorithms.

Despite these issues, a properly configured SLAM system can be extremely effective for navigation and 3D scanning. It is especially useful in environments that do not let the robot rely on GNSS positioning, such as an indoor factory floor. However, it's important to keep in mind that even a well-configured SLAM system may have errors. To correct these errors it is crucial to be able detect the effects of these errors and their implications on the SLAM process.

Mapping

The mapping function builds an image of the robot's surroundings which includes the robot, its wheels and actuators as well as everything else within its field of view. This map is used for localization, path planning and obstacle detection. This is a domain where 3D Lidars are especially helpful as they can be used as a 3D Camera (with a single scanning plane).

The map building process may take a while however, the end result pays off. The ability to build a complete and coherent map of the environment around a robot allows it to navigate with great precision, and also around obstacles.

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

There are a variety of mapping algorithms that can be utilized with LiDAR sensors. One of the most popular algorithms is Cartographer which utilizes two-phase pose graph optimization technique to adjust for drift and keep a consistent global map. It is particularly useful when paired with Odometry data.

Another alternative is GraphSLAM that employs linear equations to model the constraints of graph. The constraints are represented as an O matrix and a one-dimensional X vector, each vertex of the O matrix containing the distance to a landmark on the X vector. A GraphSLAM update is an array of additions and subtraction operations on these matrix elements with the end result being that all of the X and O vectors are updated to account for new robot observations.

Another helpful mapping algorithm is SLAM+, which combines mapping and odometry using an Extended Kalman Filter (EKF). The EKF updates not only the uncertainty of the robot's current position but also the uncertainty in the features 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 needs to be able to see its surroundings in order to avoid obstacles and reach its final point. It uses sensors such as digital cameras, infrared scans, sonar and laser radar to determine the surrounding. It also makes use of an inertial sensor to measure its position, speed and orientation. These sensors enable it to navigate in a safe manner and avoid collisions.

A range sensor is used to gauge the distance between a robot and an obstacle. The sensor can be mounted on the robot, in the vehicle, or on the pole. It is important to remember that the sensor can be affected by a myriad of factors such as wind, rain and fog. Therefore, it is crucial to calibrate the sensor before each use.

An important step in obstacle detection is to identify static obstacles. This can be done by using the results of the eight-neighbor cell clustering algorithm. This method isn't very precise due to the occlusion caused by the distance between the laser lines and the camera's angular velocity. To address this issue, multi-frame fusion was used 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 shown to improve the efficiency of processing data and reserve redundancy for subsequent navigation operations, such as path planning. This method creates an image of high-quality and reliable of the surrounding. In outdoor tests the method was compared to other obstacle detection methods like YOLOv5 monocular ranging, and VIDAR.

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

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