LiDAR Robot Navigation
LiDAR robots navigate using a combination of localization and mapping, and also path planning. This article will introduce these concepts and show how they work together using an example of a robot achieving its goal in a row of crop.
LiDAR sensors have modest power requirements, allowing them to prolong a robot's battery life and decrease the need for raw data for localization algorithms. This allows for a greater number of iterations of SLAM without overheating the GPU.
LiDAR Sensors
The sensor is the heart of the Lidar system. It emits laser beams into the environment. These light pulses strike objects and bounce back to the sensor at various angles, depending on the structure of the object. The sensor determines how long it takes each pulse to return and then utilizes that information to calculate distances. Sensors are positioned on rotating platforms, which allows them to scan the surroundings quickly and at high speeds (10000 samples per second).
LiDAR sensors are classified based on the type of sensor they're designed for, whether airborne application or terrestrial application. Airborne lidar systems are usually mounted on aircrafts, helicopters or unmanned aerial vehicles (UAVs). Terrestrial LiDAR is usually mounted on a robotic platform that is stationary.
To accurately measure distances, the sensor must be able to determine the exact location of the robot. This information is usually captured using an array of inertial measurement units (IMUs), GPS, and time-keeping electronics. LiDAR systems make use of sensors to calculate the exact location of the sensor in space and time, which is then used to build up 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. When a pulse passes through a forest canopy it will usually register multiple returns. The first one is typically attributed to the tops of the trees, while the second is associated with the surface of the ground. If the sensor records these pulses in a separate way and is referred to as discrete-return LiDAR.
The Discrete Return scans can be used to analyze the structure of surfaces. For example the forest may produce an array of 1st and 2nd returns with the final big pulse representing bare ground. The ability to separate these returns and record them as a point cloud makes it possible for the creation of detailed terrain models.
Once an 3D map of the surroundings has been created and the robot has begun to navigate using this data. This involves localization as well as creating a path to get to a navigation "goal." It also involves dynamic obstacle detection. This is the method of identifying new obstacles that are not present in the original map, and then updating the plan in line with the new obstacles.
SLAM Algorithms
SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to build an outline of its surroundings and then determine where it is in relation to the map. Engineers utilize the data for a variety of purposes, including path planning and obstacle identification.
To allow SLAM to function it requires an instrument (e.g. A computer with the appropriate software for processing the data as well as cameras or lasers are required. You'll also require an IMU to provide basic positioning information. The system can track your robot's exact location in an unknown environment.
The SLAM process is complex and many back-end solutions exist. Whatever solution you choose, a successful SLAM system requires a constant interaction between the range measurement device, the software that extracts the data, and the vehicle or robot itself. This is a highly dynamic procedure that has an almost endless amount of variance.
As the robot moves, it adds scans to its map. The SLAM algorithm then compares these scans with earlier ones using a process called scan matching. This aids in establishing loop closures. When a loop closure has been identified, the SLAM algorithm makes use of this information to update its estimate of the robot's trajectory.
The fact that the environment can change over time is a further factor that complicates SLAM. For instance, if 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 matching the two points on its map. This is when handling dynamics becomes crucial, and this is a standard characteristic of modern Lidar SLAM algorithms.
Despite these challenges however, a properly designed SLAM system is incredibly effective for navigation and 3D scanning. It is especially useful in environments that do not permit the robot to depend on GNSS for positioning, such as an indoor factory floor. However, it is important to note that even a well-configured SLAM system can be prone to mistakes. To correct these mistakes, it is important 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 surroundings. This includes the robot as well as its wheels, actuators and everything else that falls within its vision field. This map is used to perform the localization, planning of paths and obstacle detection. This is an area where 3D lidars are particularly helpful, as they can be effectively treated as an actual 3D camera (with a single scan plane).
The map building process may take a while however the results pay off. The ability to build a complete, consistent map of the robot's surroundings allows it to perform high-precision navigation, as as navigate around obstacles.
In general, the higher the resolution of the sensor the more precise will be the map. Not all robots require maps with high resolution. For instance a floor-sweeping robot may not require the same level detail as an industrial robotic system that is navigating factories of a large size.
There are many different mapping algorithms that can be utilized with LiDAR sensors. One popular algorithm is called Cartographer which employs two-phase pose graph optimization technique to correct for drift and maintain a uniform global map. It is particularly beneficial when used in conjunction with odometry data.
Another alternative is GraphSLAM which employs a system of linear equations to represent the constraints of graph. The constraints are represented as an O matrix and a the X vector, with every vertex of the O matrix representing a distance to a landmark on the X vector. A GraphSLAM update consists of a series of additions and subtraction operations on these matrix elements, with the end result being that all of the O and X vectors are updated to accommodate new robot observations.
SLAM+ is another useful mapping algorithm that combines odometry and mapping using an Extended Kalman filter (EKF). The EKF updates not only the uncertainty in the robot's current position, but also the uncertainty of the features that have been drawn by the sensor. The mapping function will utilize this information to estimate its own position, which allows it to update the base map.
Obstacle Detection
A robot needs to be able to sense its surroundings so it can avoid obstacles and reach its final point. It uses sensors like digital cameras, infrared scanners, laser radar and sonar to determine its surroundings. Additionally, it utilizes inertial sensors to measure its speed and position as well as its orientation. These sensors help it navigate safely and avoid collisions.
One important part of this process is obstacle detection that consists of the use of an IR range sensor to measure the distance between the robot and the obstacles. The sensor can be attached to the vehicle, the robot or a pole. It is crucial to keep in mind that the sensor could be affected by many factors, such as wind, rain, and fog. Therefore, it is essential to calibrate the sensor before each use.
The most important aspect of obstacle detection is identifying static obstacles, which can be accomplished using the results of the eight-neighbor-cell clustering algorithm. However, this method has a low detection accuracy due to the occlusion created by the distance between the different laser lines and the angular velocity of the camera, which makes it difficult to detect static obstacles in one frame. To address this issue multi-frame fusion was implemented to improve the effectiveness of static obstacle detection.
robot vacuum with lidar of combining roadside camera-based obstruction detection with vehicle camera has shown to improve the efficiency of processing data. It also provides the possibility of redundancy for other navigational operations like the planning of a path. This method creates a high-quality, reliable image of the environment. The method has been tested with other obstacle detection methods including YOLOv5 VIDAR, YOLOv5, and monocular ranging, in outdoor comparison experiments.
The results of the experiment proved that the algorithm could accurately identify the height and position of obstacles as well as its tilt and rotation. It was also able to determine the color and size of an object. The method also showed excellent stability and durability, even in the presence of moving obstacles.