10 Lidar Robot Navigation Tricks Experts Recommend
LiDAR Robot Navigation
LiDAR robots navigate using a combination of localization, mapping, as well as path planning. This article will introduce these concepts and explain how they interact using an example of a robot reaching a goal in a row of crops.
LiDAR sensors are low-power devices that can prolong the battery life of robots and decrease the amount of raw data needed for localization algorithms. This enables more variations of the SLAM algorithm without overheating the GPU.
LiDAR Sensors
The central component of a lidar system is its sensor which emits pulsed laser light into the surrounding. These pulses bounce off surrounding objects at different angles depending on their composition. robot vacuum lidar records the time it takes for each return and uses this information to determine distances. Sensors are mounted on rotating platforms, which allow them to scan the surrounding area quickly and at high speeds (10000 samples per second).
LiDAR sensors are classified based on whether they are designed for airborne or terrestrial application. Airborne lidar systems are usually connected to aircrafts, helicopters or unmanned aerial vehicles (UAVs). Terrestrial LiDAR systems are generally mounted on a stationary robot platform.
To accurately measure distances the sensor must always know the exact location of the robot. This information is typically captured through a combination of inertial measuring units (IMUs), GPS, and time-keeping electronics. These sensors are used by LiDAR systems in order to determine the exact position of the sensor within space and time. This information is then used to create a 3D model of the surrounding environment.
LiDAR scanners can also identify different types of surfaces, which is especially useful when mapping environments with dense vegetation. When a pulse passes a forest canopy it will usually register multiple returns. The first return is attributed to the top of the trees, while the last return is associated with the ground surface. If the sensor captures each pulse as distinct, it is known as discrete return LiDAR.
The Discrete Return scans can be used to analyze surface structure. For instance, a forest region might yield a sequence of 1st, 2nd, and 3rd returns, with a final large pulse representing the ground. The ability to divide these returns and save them as a point cloud allows to create detailed terrain models.
Once a 3D map of the environment is created and the robot is able to navigate using this information. This process involves localization and building a path that will get to a navigation "goal." It also involves dynamic obstacle detection. This process detects new obstacles that were not present in the original map and adjusts the path plan accordingly.
SLAM Algorithms
SLAM (simultaneous mapping and localization) is an algorithm which allows your robot to map its surroundings and then determine its location relative to that map. Engineers make use of this information for a variety of tasks, including planning routes and obstacle detection.
For SLAM to work it requires sensors (e.g. the laser or camera), and a computer running the appropriate software to process the data. Also, you need an inertial measurement unit (IMU) to provide basic information on your location. The result is a system that will accurately determine the location of your robot in a hazy environment.
The SLAM process is extremely complex and a variety of back-end solutions are available. No matter which solution you select for an effective SLAM it requires a constant interaction between the range measurement device and the software that collects data and also the vehicle or robot. This is a highly dynamic process that is prone to an endless amount of variance.
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 assists in establishing loop closures. If a loop closure is identified, the SLAM algorithm makes use of this information to update its estimate of the robot's trajectory.
The fact that the surroundings changes in time is another issue that makes it more difficult for SLAM. For example, if your robot is walking down an empty aisle at one point and is then confronted by pallets at the next point it will have a difficult time connecting these two points in its map. The handling dynamics are crucial in this case, and they are a part of a lot of modern Lidar SLAM algorithms.
SLAM systems are extremely efficient at navigation and 3D scanning despite the challenges. It is especially useful in environments that don't rely on GNSS for positioning for positioning, like an indoor factory floor. It is crucial to keep in mind that even a properly configured SLAM system could be affected by mistakes. It is vital to be able to spot these flaws and understand how they affect the SLAM process in order to rectify them.
Mapping
The mapping function creates a map of a robot's surroundings. This includes the robot as well as its wheels, actuators and everything else that falls within its field of vision. The map is used for location, route planning, and obstacle detection. This is a domain where 3D Lidars can be extremely useful because they can be treated as a 3D Camera (with one scanning plane).
The process of building maps may take a while however the results pay off. The ability to build a complete and consistent map of a robot's environment allows it to move with high precision, and also over obstacles.
In general, the greater the resolution of the sensor then the more accurate will be the map. 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 operating in large factories.
There are many different mapping algorithms that can be utilized with LiDAR sensors. Cartographer is a well-known algorithm that uses the two-phase pose graph optimization technique. It adjusts for drift while maintaining an accurate global map. It is especially efficient when combined with Odometry data.
Another alternative is GraphSLAM, which uses linear equations to model constraints of a graph. The constraints are represented as an O matrix, and an the X-vector. Each vertice of the O matrix contains an approximate distance from the X-vector's landmark. A GraphSLAM Update is a series additions and subtractions on these matrix elements. The end result is that all O and X Vectors are updated to reflect the latest observations made by 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 in the robot's current location, but also the uncertainty in the features that were mapped by the sensor. The mapping function can then utilize this information to better estimate its own position, which allows it to update the base map.
Obstacle Detection
A robot should be able to perceive its environment to overcome obstacles and reach its destination. It makes use of sensors such as digital cameras, infrared scanners laser radar and sonar to sense its surroundings. It also makes use of an inertial sensors to monitor its position, speed and its orientation. These sensors allow it to navigate without danger and avoid collisions.
One of the most important aspects of this process is obstacle detection that involves the use of an IR range sensor to measure the distance between the robot and the obstacles. The sensor can be placed on the robot, in a vehicle or on poles. It is important to keep in mind that the sensor may be affected by various elements, including rain, wind, and fog. It is important to calibrate the sensors prior to each use.
An important step in obstacle detection is to identify static obstacles, which can be done by using the results of the eight-neighbor-cell clustering algorithm. However, this method has a low accuracy in detecting because of the occlusion caused by the gap between the laser lines and the angular velocity of the camera which makes it difficult to detect static obstacles in one frame. To overcome this issue multi-frame fusion was employed to increase the effectiveness of static obstacle detection.
The technique of combining roadside camera-based obstruction detection with the vehicle camera has shown to improve data processing efficiency. It also reserves redundancy for other navigational tasks like path planning. This method produces an image of high-quality and reliable of the environment. In outdoor tests, the method was compared with other methods for detecting obstacles like YOLOv5 monocular ranging, VIDAR.
The results of the experiment revealed that the algorithm was able to accurately determine the height and location of an obstacle, in addition to its tilt and rotation. It was also able detect the color and size of an object. The method also exhibited solid stability and reliability even when faced with moving obstacles.