The Reason Why Everyone Is Talking About Lidar Robot Navigation Right Now

LiDAR Robot Navigation

LiDAR robots navigate by using the combination of localization and mapping, as well as path planning. This article will present these concepts and show how they function together with a simple example of the robot achieving its goal in a row of crop.

LiDAR sensors have modest power requirements, allowing them to extend the battery life of a robot and reduce the raw data requirement for 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 light pulses bounce off the surrounding objects at different angles based on their composition. The sensor determines how long it takes for each pulse to return and uses that information 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 are classified based on the type of sensor they're designed for, whether applications in the air or on land. Airborne lidar systems are commonly mounted on aircrafts, helicopters, or unmanned aerial vehicles (UAVs). Terrestrial LiDAR systems are typically placed 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 using a combination of inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems make use of these sensors to compute the exact location of the sensor in time and space, which is then used to create a 3D map of the surrounding area.

LiDAR scanners can also be used to detect different types of surface which is especially useful when mapping environments that have dense vegetation. When a pulse passes through a forest canopy, it is likely to register multiple returns. The first return is usually attributed to the tops of the trees, while the last is attributed with the surface of the ground. If the sensor captures these pulses separately, it is called discrete-return LiDAR.

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

Once an 3D model of the environment is created, the robot will be able to use this data to navigate. This involves localization and making a path that will take it to a specific navigation "goal." It also involves dynamic obstacle detection. This process detects new obstacles that were not present in the original map and then updates the plan of travel according to the new obstacles.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its surroundings, and then identify its location relative to that map. Engineers make use of this information to perform a variety of tasks, including planning routes and obstacle detection.

To use SLAM your robot has to have a sensor that provides range data (e.g. laser or camera), and a computer running the appropriate software to process the data. You also need an inertial measurement unit (IMU) to provide basic information about your position. The system will be able to track your robot's location accurately in a hazy environment.

The SLAM system is complicated and there are many different back-end options. Whatever solution you choose for your SLAM system, a successful SLAM system requires a constant interplay between the range measurement device and the software that extracts the data and the vehicle or robot itself. This is a dynamic procedure that is almost indestructible.

When the robot moves, it adds scans to its map. The SLAM algorithm then compares these scans to previous ones using a process called scan matching. This assists in establishing loop closures. The SLAM algorithm updates its estimated robot trajectory when loop closures are discovered.

Another factor that makes SLAM is the fact that the surrounding changes in time. For instance, if your robot is walking along an aisle that is empty at one point, but then comes across a pile of pallets at a different point it might have trouble connecting the two points on its map. This is where handling dynamics becomes critical, and this is a standard feature of modern Lidar SLAM algorithms.

SLAM systems are extremely efficient in navigation and 3D scanning despite the challenges. It is especially useful in environments that don't allow the robot to depend on GNSS 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. To fix these issues, it is important to be able to recognize them and comprehend their impact on the SLAM process.

Mapping

The mapping function builds an image of the robot's surroundings which includes the robot itself as well as its wheels and actuators as well as everything else within the area of view. This map is used to perform localization, path planning and obstacle detection. This is an area in which 3D Lidars are especially helpful, since they can be used as an 3D Camera (with only one scanning plane).

Map creation is a long-winded process but it pays off in the end. The ability to build a complete and coherent map of the environment around a robot allows it to move with high precision, as well as around obstacles.

The greater the resolution of the sensor, the more precise will be the map. Not all robots require high-resolution maps. For example, a floor sweeping robot might not require the same level detail as a robotic system for industrial use navigating large factories.

There are many different mapping algorithms that can be utilized with LiDAR sensors. Cartographer is a popular algorithm that employs the two-phase pose graph optimization technique. It corrects for drift while maintaining an unchanging global map. It is especially efficient when combined with odometry data.

Another alternative is GraphSLAM which employs linear equations to model the constraints of graph. The constraints are modeled as an O matrix and an one-dimensional X vector, each vertex of the O matrix representing the distance to a point on the X vector. 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 take into account 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 of the robot's current position but also the uncertainty of the features drawn by the sensor. This information can be utilized by the mapping function to improve its own estimation of its location, and also to update the map.

Obstacle Detection

A robot needs to be able to see its surroundings so it can avoid obstacles and get to its desired point. It uses sensors like digital cameras, infrared scanners laser radar and sonar to detect its environment. It also makes use of an inertial sensors to monitor its speed, location and the direction. These sensors assist it in navigating in a safe and secure manner and avoid collisions.

A range sensor is used to determine the distance between a robot and an obstacle. The sensor can be placed on the robot, inside the vehicle, or on poles. It is crucial to keep in mind that the sensor can be affected by a variety of factors, such as wind, rain, and fog. It is crucial to calibrate the sensors before each use.

The results of the eight neighbor cell clustering algorithm can be used to identify static obstacles. However, this method is not very effective in detecting obstacles due to the occlusion created by the gap between the laser lines and the speed of the camera's angular velocity making it difficult to recognize static obstacles in one frame. To solve this issue, a technique of multi-frame fusion has been used to increase the detection accuracy of static obstacles.

The method of combining roadside unit-based as well as obstacle detection using a vehicle camera has been proven to improve the data processing efficiency and reserve redundancy for future navigational tasks, like path planning. The result of this method is a high-quality picture of the surrounding environment that is more reliable than one frame. In  robot vacuum with lidar , the method was compared with other methods of obstacle detection such as YOLOv5 monocular ranging, VIDAR.

The results of the experiment proved that the algorithm was able to correctly identify the location and height of an obstacle, as well as its rotation and tilt. It also had a great ability to determine the size of an obstacle and its color. The method also exhibited solid stability and reliability, even in the presence of moving obstacles.