LiDAR Robot Navigation
LiDAR robot navigation is a complicated combination of localization, mapping and path planning. This article will introduce the concepts and demonstrate how they work using an easy example where the robot reaches a goal within a plant row.
LiDAR sensors are low-power devices which can prolong the battery life of robots and decrease the amount of raw data needed for localization algorithms. This allows for a greater number of iterations of SLAM without overheating the GPU.
LiDAR Sensors
The central component of a lidar system is its sensor which emits laser light in the environment. The light waves hit objects around and bounce back to the sensor at various angles, depending on the structure of the object. The sensor monitors the time it takes for each pulse to return and then uses that data to calculate distances. Sensors are placed on rotating platforms, which allow them to scan the surrounding area quickly and at high speeds (10000 samples per second).
LiDAR sensors are classified according to their intended airborne or terrestrial application. Airborne lidar systems are usually mounted on aircrafts, helicopters or unmanned aerial vehicles (UAVs). Terrestrial LiDAR systems are usually placed on a stationary robot platform.
To accurately measure distances the sensor must always know the exact location of the robot. This information is recorded by a combination of an inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are employed by LiDAR systems to determine the exact location of the sensor in space and time. This information is then used to build a 3D model of the surrounding environment.
LiDAR scanners can also be used to identify different surface types and types of surfaces, which is particularly useful for mapping environments with dense vegetation. For instance, when the pulse travels through a forest canopy it is common for it to register multiple returns. The first return is usually attributed to the tops of the trees, while the second one is attributed to the ground's surface. If the sensor records these pulses separately, it is called discrete-return LiDAR.
Distinte return scanning can be useful in studying the structure of surfaces. For instance, a forest region might yield the sequence of 1st 2nd, and 3rd returns, with a last large pulse representing the bare ground. The ability to divide these returns and save them as a point cloud makes it possible to create detailed terrain models.
Once a 3D model of the environment is built, the robot will be equipped to navigate. This process involves localization, building the path needed to reach a navigation 'goal and dynamic obstacle detection. The latter is the process of identifying new obstacles that are not present on the original map and updating the path plan accordingly.
SLAM Algorithms
SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its environment and then determine its location relative to that map. Engineers utilize the information to perform a variety of purposes, including planning a path and identifying obstacles.
To be able to use SLAM your robot has to have a sensor that gives range data (e.g. A computer that has the right software to process the data and cameras or lasers are required. Also, you need an inertial measurement unit (IMU) to provide basic information about your position. The result is a system that will accurately track the location of your robot in an unspecified environment.
The SLAM process is extremely complex, and many different back-end solutions are available. No matter which one you choose the most effective SLAM system requires constant interaction between the range measurement device and the software that extracts the data and the vehicle or robot. This is a highly dynamic procedure that has an almost unlimited amount of variation.
As the robot moves about the area, it adds new scans to its map. The SLAM algorithm then compares these scans to earlier ones using a process known as scan matching. This helps to establish loop closures. The SLAM algorithm is updated with its estimated robot trajectory once the loop has been closed identified.
Another factor that complicates SLAM is the fact that the surrounding changes in time. For example, if your robot travels down an empty aisle at one point, and is then confronted by pallets at the next spot, it will have difficulty finding these two points on its map. This is when handling dynamics becomes important, and this is a standard feature of the modern Lidar SLAM algorithms.
SLAM systems are extremely effective in 3D scanning and navigation despite these limitations. It is particularly beneficial in situations where the robot can't depend on GNSS to determine its position for example, an indoor factory floor. However, it is important to remember that even a well-designed SLAM system can experience mistakes. To fix lidar navigation robot vacuum is essential to be able to recognize them and comprehend their impact on the SLAM process.
Mapping
The mapping function creates a map for a robot's environment. This includes the robot as well as its wheels, actuators and everything else within its field of vision. This map is used to perform localization, path planning, and obstacle detection. This is an area where 3D lidars are extremely helpful since they can be effectively treated as a 3D camera (with one scan plane).
Map building is a long-winded process but it pays off in the end. The ability to build a complete, coherent map of the robot's environment allows it to carry out high-precision navigation as well being able to navigate around obstacles.
As a general rule of thumb, the higher resolution the sensor, the more precise the map will be. Not all robots require high-resolution maps. For example, a floor sweeping robot may not require the same level detail as an industrial robotic system that is navigating factories of a large size.
To this end, there are a number of different mapping algorithms that can be used with LiDAR sensors. Cartographer is a very popular algorithm that uses the two-phase pose graph optimization technique. It adjusts for drift while maintaining an unchanging global map. It is particularly useful when paired with Odometry data.
Another option is GraphSLAM which employs a system of linear equations to model constraints of a graph. The constraints are represented by an O matrix, and a vector X. Each vertice of the O matrix contains the distance to an X-vector landmark. A GraphSLAM Update is a sequence of additions and subtractions on these matrix elements. The end result is that all O and X Vectors are updated in order to reflect the latest observations made by the robot.
SLAM+ is another useful mapping algorithm that combines odometry with 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 that were mapped by the sensor. The mapping function is able to utilize this information to better estimate its own position, allowing it to update the underlying map.
Obstacle Detection
A robot should be able to see its surroundings to avoid obstacles and reach its goal. It uses sensors such as digital cameras, infrared scans laser radar, and sonar to sense the surroundings. It also uses inertial sensor to measure its position, speed and the direction. These sensors help it navigate without danger and avoid collisions.
One of the most important aspects of this process is the detection of obstacles that involves the use of a range sensor to determine the distance between the robot and the obstacles. The sensor can be attached to the robot, a vehicle or even a pole. It is crucial to keep in mind that the sensor could be affected by various elements, including rain, wind, and fog. It is crucial to calibrate the sensors prior every use.
The results of the eight neighbor cell clustering algorithm can be used to detect static obstacles. This method is not very accurate because of the occlusion caused by the distance between laser lines and the camera's angular speed. To address this issue, multi-frame fusion was used to increase the effectiveness of static obstacle detection.
The method of combining roadside camera-based obstacle detection with the vehicle camera has shown to improve the efficiency of processing data. It also reserves redundancy for other navigational tasks such as the planning of a path. This method provides a high-quality, reliable image of the environment. In outdoor comparison tests the method was compared with other obstacle detection methods such as YOLOv5 monocular ranging, and VIDAR.
The experiment results revealed that the algorithm was able to accurately determine the height and location of an obstacle, as well as its tilt and rotation. It was also able identify the color and size of the object. The method also showed excellent stability and durability, even when faced with moving obstacles.