• 목록
• 답변
• 글쓰기
• 게시판 리스트 옵션
  • 수정
  • 삭제

LiDAR Robot Navigation

LiDAR robot navigation is a complicated combination of localization, mapping and path planning. This article will explain these concepts and explain how they work together using an easy example of the robot reaching a goal in the middle of a row of crops.

LiDAR sensors are low-power devices that prolong the life of batteries on a robot and reduce the amount of raw data needed for localization algorithms. This allows for a greater number of iterations of the SLAM algorithm without overheating the GPU.

LiDAR Sensors

The sensor is the heart of Lidar systems. It releases laser pulses into the surrounding. These pulses hit surrounding objects and bounce back to the sensor at a variety of angles, based on the structure of the object. The sensor monitors the time it takes each pulse to return and then uses that information to calculate distances. Sensors are mounted on rotating platforms, which allow them to scan the area around them quickly and at high speeds (10000 samples per second).

lidar mapping robot vacuum sensors can be classified according to whether they're designed for use in the air or on the ground. Airborne lidars are usually mounted on helicopters or an unmanned aerial vehicles (UAV). Terrestrial LiDAR is usually installed on a stationary robot platform.

To accurately measure distances, the sensor needs to know the exact position of the robot at all times. This information is recorded by a combination of an inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are used by lidar vacuum mop systems to determine the exact position of the sensor within the space and time. The information gathered is used to build a 3D model of the environment.

LiDAR scanners can also be used to recognize different types of surfaces, which is particularly beneficial for mapping environments with dense vegetation. For example, when a pulse passes through a forest canopy it will typically register several returns. The first one is typically attributable to the tops of the trees, while the second is associated with the ground's surface. If the sensor captures these pulses separately, it is called discrete-return LiDAR.

Discrete return scans can be used to determine surface structure. For instance, a forest region might yield a sequence of 1st, 2nd, and 3rd returns, with a final large pulse that represents the ground. The ability to separate and record these returns as a point-cloud allows for detailed models of terrain.

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

SLAM Algorithms

SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to build an image of its surroundings and then determine where it is in relation to the map. Engineers utilize this information to perform a variety of tasks, including path planning and obstacle detection.

To allow SLAM to work the robot vacuum obstacle avoidance lidar needs sensors (e.g. the laser or camera) and a computer running the right software to process the data. You also need an inertial measurement unit (IMU) to provide basic positional information. The result is a system that can accurately track the location of your robot in an unspecified environment.

The SLAM process is complex and many back-end solutions are available. Regardless of which solution you choose, a successful SLAM system requires a constant interplay between the range measurement device, the software that extracts the data, and the vehicle or cheapest robot vacuum with lidar. This is a highly dynamic procedure that can have an almost infinite amount of variability.

As the robot moves around, it adds new scans to its map. The SLAM algorithm then compares these scans to the previous ones using a method called scan matching. This allows loop closures to be created. The SLAM algorithm updates its robot's estimated trajectory when a loop closure has been detected.

Another issue that can hinder SLAM is the fact that the environment changes over time. For example, if your robot travels through an empty aisle at one point and then encounters stacks of pallets at the next location, it will have difficulty finding these two points on its map. The handling dynamics are crucial in this scenario, and they are a characteristic of many modern Lidar SLAM algorithm.

Despite these issues however, a properly designed SLAM system is extremely efficient for navigation and 3D scanning. It is especially beneficial in environments that don't let the robot rely on GNSS-based positioning, such as an indoor factory floor. It's important to remember that even a properly configured SLAM system may experience mistakes. To correct these errors, 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 an image of the robot's surrounding, which includes the robot including its wheels and actuators, and everything else in its field of view. The map is used to perform localization, path planning and obstacle detection. This is an area where 3D lidars are extremely helpful, as they can be used as the equivalent of a 3D camera (with one scan plane).

The process of creating maps can take some time however, the end result pays off. The ability to build a complete and coherent map of the robot's surroundings allows it to move with high precision, and also over obstacles.

The greater the resolution of the sensor, then the more accurate will be the map. Not all robots require high-resolution maps. For instance floor sweepers might 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 employed with lidar vacuum robot sensors. One of the most well-known algorithms is Cartographer, which uses a two-phase pose graph optimization technique to correct for drift and maintain an accurate global map. It is particularly efficient when combined with Odometry data.

GraphSLAM is another option, which utilizes a set of linear equations to represent the constraints in a diagram. The constraints are represented by an O matrix, as well as an X-vector. Each vertice of the O matrix is a distance from an X-vector landmark. A GraphSLAM Update is a series subtractions and additions to these matrix elements. The end result is that all O and X Vectors are updated in order to account for the new observations made by 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 position as well as the uncertainty of the features recorded 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 needs to be able to perceive its environment so that it can overcome obstacles and reach its destination. It makes use of sensors like digital cameras, infrared scans, laser radar, and sonar to sense the surroundings. In addition, it uses inertial sensors that measure its speed, position and orientation. These sensors allow it to navigate safely and avoid collisions.

One of the most important aspects of this process is obstacle detection that involves the use of sensors to measure the distance between the robot and obstacles. The sensor can be attached to the robot, a vehicle or a pole. It is crucial to remember that the sensor could be affected by a myriad of factors, including wind, rain and fog. Therefore, it is important to calibrate the sensor prior to every use.

A crucial step in obstacle detection is identifying static obstacles. This can be accomplished 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 speed. To address this issue multi-frame fusion was implemented to increase the accuracy of static obstacle detection.

The technique of combining roadside camera-based obstruction detection with a vehicle camera has proven to increase the efficiency of data processing. It also provides the possibility of redundancy for other navigational operations such as planning a path. This method produces a high-quality, reliable image of the environment. The method has been tested against other obstacle detection methods including YOLOv5 VIDAR, YOLOv5, as well as monocular ranging, in outdoor comparative tests.

The results of the experiment showed that the algorithm could accurately identify the height and location of an obstacle as well as its tilt and rotation. It also had a great ability to determine the size of the obstacle and its color. The method was also robust and stable even when obstacles were moving.