LiDAR Robot Navigation

lidar vacuum cleaner robot navigation, visit here, is a complex combination of localization, mapping and path planning. This article will explain the concepts and show how they work by using a simple example where the robot reaches an objective within a plant row.

LiDAR sensors are low-power devices that can extend the battery life of a robot and reduce the amount of raw data needed for localization algorithms. This enables more iterations of the SLAM algorithm without overheating the GPU.

LiDAR Sensors

The heart of lidar systems is their sensor, which emits pulsed laser light into the surrounding. These pulses bounce off objects around them at different angles based on their composition. The sensor monitors the time it takes for each pulse to return and uses that data to calculate distances. The sensor is typically mounted on a rotating platform allowing it to quickly scan the entire area at high speed (up to 10000 samples per second).

LiDAR sensors can be classified based on whether they're designed for use in the air or on the ground. Airborne lidars are typically connected to helicopters or an unmanned aerial vehicles (UAV). Terrestrial LiDAR systems are usually placed on a stationary robot platform.

To accurately measure distances the sensor must be able to determine the exact location of the robot. This information is usually gathered using a combination of inertial measurement units (IMUs), GPS, and time-keeping electronics. LiDAR systems use these sensors to compute the precise location of the sensor in space and time, which is then used to create an 3D map of the environment.

LiDAR scanners are also able to identify different kinds of surfaces, which is especially beneficial when mapping environments with dense vegetation. When a pulse crosses a forest canopy, it is likely to register multiple returns. The first return is usually associated with the tops of the trees while the last is attributed with the surface of the ground. If the sensor captures each pulse as distinct, it is referred to as discrete return LiDAR.

Distinte return scans can be used to study surface structure. For instance, a forest region may yield an array of 1st and 2nd returns, with the final large pulse representing the ground. The ability to separate and record these returns in a point-cloud allows for detailed models of terrain.

Once an 3D model of the environment is constructed, the robot will be able to use this data to navigate. This involves localization, constructing a path to reach a goal for navigation and dynamic obstacle detection. This is the method of identifying new obstacles that are not present in the map originally, and adjusting the path plan accordingly.

SLAM Algorithms

SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to construct a map of its environment and then determine the location of its position relative to the map. Engineers make use of this 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 provides range data (e.g. A computer that has the right software for processing the data, as well as either a camera or laser are required. Also, you need an inertial measurement unit (IMU) to provide basic information about your position. The result is a system that can precisely track the position of your robot in an unknown environment.

The SLAM system is complex and there are a variety of back-end options. No matter which one you choose, a successful SLAM system requires a constant interaction between the range measurement device and the software that extracts the data and the vehicle or robot. This is a highly dynamic process that has an almost unlimited amount of variation.

As the robot moves it adds scans to its map. The SLAM algorithm then compares these scans with earlier ones using a process known as scan matching. This allows loop closures to be created. The SLAM algorithm updates its estimated robot trajectory once a loop closure has been detected.

Another issue that can hinder SLAM is the fact that the environment changes in time. If, for instance, your robot is navigating an aisle that is empty at one point, and then encounters a stack of pallets at another point it might have trouble finding the two points on its map. This is where handling dynamics becomes critical, and this is a standard characteristic of modern Lidar SLAM algorithms.

Despite these challenges, a properly configured SLAM system is extremely efficient for navigation and 3D scanning. It is particularly beneficial in situations that don't rely on GNSS for positioning for example, an indoor factory floor. It is important to keep in mind that even a well-designed SLAM system may experience errors. It is crucial to be able to spot these flaws and understand how they impact the SLAM process in order to fix them.

Mapping

The mapping function creates a map of a robot's surroundings. This includes the robot vacuum lidar and its wheels, actuators, and everything else within its vision field. The map is used for localization, route planning and obstacle detection. This is an area where 3D lidars are extremely helpful, as they can be used like the equivalent of a 3D camera (with only one scan plane).

Map creation can be a lengthy process however, it is worth it in the end. The ability to create a complete, consistent map of the vacuum robot with lidar's environment allows it to conduct high-precision navigation, as well as navigate around obstacles.

The higher the resolution of the sensor, then the more accurate will be the map. Not all robots require high-resolution maps. For example floor sweepers may not require the same level of detail as an industrial robotics system that is navigating factories of a large size.

There are a variety of mapping algorithms that can be used with LiDAR sensors. One of the most popular algorithms is Cartographer which utilizes the two-phase pose graph optimization technique to correct for drift and create an accurate global map. It is especially efficient when combined with odometry data.

Another option is GraphSLAM that employs linear equations to model the constraints of a graph. The constraints are represented as an O matrix and an one-dimensional X vector, each vertice of the O matrix representing a distance to a landmark on the X vector. A GraphSLAM update consists of the addition and subtraction operations on these matrix elements, and the result is that all of the X and O vectors are updated to account for new information about the robot.

Another useful mapping algorithm is SLAM+, which combines mapping and odometry 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 improve its own location, allowing it to update the underlying map.

Obstacle Detection

A robot must be able to sense its surroundings in order to avoid obstacles and reach its goal point. It makes use of sensors such as digital cameras, infrared scanners laser radar and sonar to detect its environment. Additionally, it employs inertial sensors that measure its speed and position as well as its orientation. These sensors enable it to navigate in a safe manner and avoid collisions.

A key element of this process is obstacle detection that consists of the use of a range sensor to determine the distance between the robot and obstacles. The sensor can be placed on the robot, inside the vehicle, or on a pole. It is crucial to keep in mind that the sensor may be affected by various elements, including wind, rain, and fog. It is important to calibrate the sensors before every use.

A crucial step in obstacle detection is to identify static obstacles. This can be accomplished by using the results of the eight-neighbor cell clustering algorithm. However, this method has a low accuracy in detecting due to the occlusion caused by the gap between the laser lines and the speed of the camera's angular velocity making it difficult to recognize static obstacles in a single frame. To solve this issue, a method of multi-frame fusion was developed to improve the detection accuracy of static obstacles.

The technique of combining roadside camera-based obstacle detection with vehicle camera has shown to improve the efficiency of data processing. It also provides redundancy for other navigational tasks like the planning of a path. This method creates an image of high-quality and reliable of the surrounding. The method has been compared against other obstacle detection methods like YOLOv5 VIDAR, YOLOv5, and monocular ranging, in outdoor tests of comparison.

The experiment results showed that the algorithm could accurately determine the height and location of obstacles as well as its tilt and rotation. It also had a good ability to determine the size of obstacles and its color. The method also demonstrated excellent stability and durability even in the presence of moving obstacles.