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LiDAR Robot Navigation

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

LiDAR sensors are low-power devices that can prolong the battery life of robots and reduce the amount of raw data required to run localization algorithms. This allows for more versions of the SLAM algorithm without overheating the GPU.

LiDAR Sensors

The sensor is at the center of the Lidar system. It emits laser pulses into the environment. These light pulses bounce off surrounding objects in different angles, based on their composition. The sensor measures how long it takes for each pulse to return and then utilizes that information to determine distances. Sensors are placed on rotating platforms, which allow them to scan the surroundings quickly and at high speeds (10000 samples per second).

LiDAR sensors are classified based on their intended applications in the air or on land. Airborne lidar systems are usually connected to aircrafts, helicopters or unmanned aerial vehicles (UAVs). Terrestrial LiDAR systems are typically mounted on a static Vacuum Robot Lidar platform.

To accurately measure distances, the sensor must be able to determine the exact location of the robot. This information is captured by a combination of an inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems make use of sensors to calculate the exact location of the sensor in space and time. This information is later used to construct a 3D map of the surrounding area.

LiDAR scanners can also identify different types 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 common for it to register multiple returns. Typically, the first return is associated with the top of the trees while the last return is attributed to the ground surface. If the sensor records these pulses in a separate way, it is called discrete-return LiDAR.

Discrete return scans can be used to determine the structure of surfaces. For instance the forest may produce an array of 1st and 2nd returns with the final big pulse representing bare ground. The ability to separate and record these returns in a point-cloud allows for detailed models of terrain.

Once a 3D map of the surroundings is created and the robot is able to navigate based on this data. This involves localization, creating an appropriate path to reach a goal for navigation and dynamic obstacle detection. This process identifies new obstacles not included in the original map and updates the path plan according to the new obstacles.

SLAM Algorithms

SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to create an image of its surroundings and then determine where it is in relation to the map. Engineers use the data for a variety of tasks, such as the planning of routes and obstacle detection.

To use SLAM your robot has to have a sensor that gives range data (e.g. the laser or camera) and a computer that has the appropriate software to process the data. You will also require an inertial measurement unit (IMU) to provide basic information on your location. The result is a system that can accurately track the location of your robot in an unspecified environment.

The SLAM system is complex and there are many different back-end options. Whatever solution you choose for your SLAM system, a successful SLAM system requires a constant interplay between the range measurement device and the software that collects the data, and the vehicle or robot. This is a highly dynamic process that can have an almost unlimited amount of variation.

As the robot moves, it adds scans to its map. The SLAM algorithm compares these scans to the previous ones using a process called scan matching. This allows loop closures to be created. If a loop closure is detected it is then the SLAM algorithm utilizes this information to update its estimate of the robot's trajectory.

The fact that the surroundings can change over time is a further factor that can make it difficult to use SLAM. For instance, if your robot travels down an empty aisle at one point, and then encounters stacks of pallets at the next spot, it will have difficulty finding these two points on its map. This is where handling dynamics becomes crucial and is a standard feature of modern Lidar SLAM algorithms.

Despite these difficulties, a properly-designed SLAM system can be extremely effective for navigation and 3D scanning. It is particularly useful in environments that do not permit the robot to depend on GNSS for positioning, such as an indoor factory floor. However, it's important to note that even a well-configured SLAM system can be prone to mistakes. It is vital to be able recognize these errors and understand how they affect the SLAM process in order to rectify them.

Mapping

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

Map creation is a long-winded process, but it pays off in the end. The ability to create a complete and coherent map of the robot's surroundings allows it to move with high precision, as well as over obstacles.

The higher the resolution of the sensor then the more accurate will be the map. However it is not necessary for all robots to have maps with high resolution. For instance, a floor sweeper may not need the same degree of detail as a industrial robot that navigates factories of immense size.

This is why there are many different mapping algorithms for use with lidar product sensors. One of the most popular algorithms is Cartographer which utilizes two-phase pose graph optimization technique to adjust for drift and keep a uniform global map. It is especially beneficial when used in conjunction with Odometry data.

Another option is GraphSLAM which employs linear equations to model constraints of graph. The constraints are represented as an O matrix and a one-dimensional X vector, each vertex of the O matrix representing the distance to a landmark on the X vector. A GraphSLAM update is the addition and subtraction operations on these matrix elements with the end result being that all of the O and X vectors are updated to account for new observations of 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 of the robot's current position but also the uncertainty of the features that were mapped 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 vacuum lidar needs to be able to perceive its surroundings in order to avoid obstacles and get to its desired point. It employs sensors such as digital cameras, infrared scans, laser radar, and sonar to determine the surrounding. In addition, it uses inertial sensors that measure its speed and position as well as its orientation. These sensors enable it to navigate safely and avoid collisions.

One important part of this process is the detection of obstacles, which involves the use of sensors to measure the distance between the robot and the obstacles. The sensor can be mounted on the robot, inside the vehicle, or on a pole. It is crucial to keep in mind that the sensor can be affected by a variety of factors, such as wind, rain, and fog. Therefore, it is essential to calibrate the sensor before every use.

A crucial step in obstacle detection is the identification of static obstacles. This can be accomplished by using the results of the eight-neighbor cell clustering algorithm. This method isn't particularly accurate because of the occlusion induced by the distance between the laser lines and the camera's angular velocity. To overcome this issue, multi-frame fusion was used to increase the accuracy of static obstacle detection.

The method of combining roadside unit-based and obstacle detection by a vehicle camera has been proven to improve the data processing efficiency and reserve redundancy for further navigational tasks, like path planning. The result of this method is a high-quality picture of the surrounding area that is more reliable than one frame. In outdoor comparison experiments the method was compared against other obstacle detection methods like YOLOv5 monocular ranging, and VIDAR.

The results of the study showed that the algorithm was able to accurately determine the height and location of an obstacle, as well as its tilt and rotation. It also had a good ability to determine the size of an obstacle and its color. The method was also reliable and stable even when obstacles were moving.