Lefant LS1 Pro: Advanced Lidar - Real-time Robotic Mapping Robot Navigation

LiDAR robot navigation is a sophisticated combination of mapping, localization and path planning. This article will explain the concepts and explain how they function using an example in which the robot reaches a goal within a plant row.

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

LiDAR Sensors

The heart of a lidar system is its sensor that emits pulsed laser light into the surrounding. These pulses hit surrounding objects and bounce back to the sensor at various angles, depending on the structure of the object. The sensor records the time it takes to return each time, which is then used to calculate distances. The sensor is typically placed on a rotating platform which allows it to scan the entire area at high speeds (up to 10000 samples per second).

LiDAR sensors can be classified according to the type of sensor they're designed for, whether airborne application or terrestrial application. Airborne lidars are typically connected to helicopters or an unmanned aerial vehicle (UAV). Terrestrial LiDAR systems are usually placed on a stationary robot platform.

To accurately measure distances, the sensor must be able to determine the exact location of the robot. This information is usually gathered using an array of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are used by LiDAR systems in order to determine the precise location of the sensor within space and time. The information gathered is used to build a 3D model of the surrounding environment.

LiDAR scanners are also able to recognize different types of surfaces, which is particularly useful for mapping environments with dense vegetation. When a pulse passes a forest canopy it will usually produce multiple returns. The first return is usually attributable to the tops of the trees, while the last is attributed with the ground's surface. If the sensor records these pulses in a separate way, it is called discrete-return LiDAR.

The use of Discrete Return scanning can be helpful in analyzing surface structure. For instance forests can produce a series of 1st and 2nd returns with the final large pulse representing bare ground. The ability to separate and store these returns in a point-cloud allows for detailed models of terrain.

Once a 3D map of the environment has been built, the robot can begin to navigate based on this data. This involves localization as well as creating a path to reach a navigation "goal." It also involves dynamic obstacle detection. This process detects new obstacles that are not listed in the map's original version and then updates the plan of travel in line with the new obstacles.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm which allows your robot to map its surroundings, and then determine its location in relation to that map. Engineers utilize the data for a variety of tasks, including the planning of routes and obstacle detection.

To allow SLAM to function, your robot must have a sensor (e.g. A computer that has the right software for processing the data, as well as a camera or a laser are required. You also need an inertial measurement unit (IMU) to provide basic information about your position. The system can track your robot's exact location in an unknown environment.

The SLAM system is complicated and there are many different back-end options. Whatever solution you select the most effective SLAM system requires a constant interaction between the range measurement device, LiDAR Robot Navigation the software that extracts the data and the vehicle or robot. This is a highly dynamic process that can have an almost infinite amount of variability.

When the robot moves, it adds scans to its map. The SLAM algorithm compares these scans with prior ones using a process known as scan matching. This allows loop closures to be identified. When a loop closure is discovered when loop closure is detected, the SLAM algorithm makes use of this information to update its estimate of the robot's trajectory.

Another factor that complicates SLAM is the fact that the surrounding changes in time. If, for example, your robot is walking along an aisle that is empty at one point, but then encounters a stack of pallets at another point it might have trouble 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 algorithm.

SLAM systems are extremely effective in 3D scanning and navigation despite these limitations. It is especially useful in environments that do not permit the robot to rely on GNSS-based positioning, like an indoor factory floor. It's important to remember that even a properly-configured SLAM system could be affected by errors. It is crucial to be able to detect these issues and comprehend how they affect the SLAM process in order to rectify them.

Mapping

The mapping function creates an image of the robot's surroundings which includes the robot, 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 an area where 3D lidars can be extremely useful, as they can be effectively treated as the equivalent of a 3D camera (with only one scan plane).

The process of building maps can take some time, but the results pay off. The ability to build a complete and coherent map of a robot's environment allows it to move with high precision, and also over obstacles.

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

This is why there are a variety of different mapping algorithms that can be used with LiDAR sensors. Cartographer is a very popular algorithm that employs the two-phase pose graph optimization technique. It corrects for drift while maintaining a consistent global map. It is particularly efficient when combined with odometry data.

GraphSLAM is a different option, which utilizes a set of linear equations to represent constraints in diagrams. The constraints are modelled 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 consists of an array of additions and subtraction operations on these matrix elements, and the result is that all of the X and O vectors are updated to reflect new observations of the robot.

Another efficient mapping algorithm is SLAM+, which combines odometry and mapping using an Extended Kalman Filter (EKF). The EKF updates the uncertainty of the robot's location as well as the uncertainty of the features mapped by the sensor. The mapping function is able to utilize this information to estimate its own position, which allows it to update the base map.

Obstacle Detection

A robot must be able to sense its surroundings to avoid obstacles and reach its final point. It employs sensors such as digital cameras, infrared scans sonar, laser radar and others to sense the surroundings. It also uses inertial sensors to monitor its position, speed and the direction. These sensors allow it to navigate safely and avoid collisions.

One important part of this process is the detection of obstacles, which involves the use of an IR range sensor to measure the distance between the robot and obstacles. The sensor can be positioned on the robot, in a vehicle or on a pole. It is important to remember that the sensor is affected by a variety of factors such as wind, rain and fog. It is essential to calibrate the sensors before every use.

A crucial step in obstacle detection is the identification of static obstacles, which can be accomplished using the results of the eight-neighbor-cell clustering algorithm. This method isn't very accurate because of the occlusion created by the distance between laser lines and the camera's angular speed. To overcome this problem multi-frame fusion was employed to improve the accuracy of static obstacle detection.

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

The results of the test showed that the algorithm was able accurately identify the location and height of an obstacle, in addition to its tilt and rotation. It was also able to identify the size and color of the object. The method also showed good stability and robustness even in the presence of moving obstacles.