LiDAR Robot Navigation

LiDAR robot navigation is a complicated combination of localization, mapping and path planning. This article will outline the concepts and explain how they function using an example in which the robot is able to reach an objective within a row of plants.

LiDAR sensors have modest power requirements, allowing them to prolong the life of a robot's battery 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 core of a Lidar system. It emits laser pulses into the environment. These pulses hit surrounding objects and bounce back to the sensor at various angles, depending on the composition of the object. The sensor records the time it takes for each return and then uses it to calculate distances. Sensors are placed on rotating platforms that allow them to scan the surrounding area quickly and at high speeds (10000 samples per second).

LiDAR sensors are classified according to whether they are designed for airborne or terrestrial application. Airborne lidar systems are typically connected to aircrafts, helicopters or unmanned aerial vehicles (UAVs). Terrestrial LiDAR systems are typically 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 by a combination of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are utilized by LiDAR systems to determine the exact location of the sensor within space and time. The information gathered is used to create a 3D representation of the surrounding environment.

LiDAR scanners are also able to identify various types of surfaces which is particularly beneficial when mapping environments with dense vegetation. For example, when the pulse travels through a canopy of trees, it is likely to register multiple returns. The first return is attributable to the top of the trees, while the final return is related to the ground surface. If the sensor captures these pulses separately and is referred to as discrete-return LiDAR.

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

Once a 3D map of the surroundings has been created, the robot can begin to navigate using this information. This involves localization, building the path needed to get to a destination and dynamic obstacle detection. This process detects new obstacles that are not listed in the original map and adjusts 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 in relation to the map. Engineers use the data for a variety of purposes, including path planning and obstacle identification.

For SLAM to work it requires an instrument (e.g. the laser or camera), and a computer that has the right software to process the data. Also, you need an inertial measurement unit (IMU) to provide basic positional information. The result is a system that will precisely track the position of your robot in an unspecified environment.

The SLAM system is complicated and there are a variety of back-end options. No matter which solution you choose to implement a successful SLAM it requires constant communication between the range measurement device and the software that extracts data and the robot or vehicle. This is a dynamic process with almost infinite variability.

As the robot moves about the area, it adds new scans to its map. The SLAM algorithm compares these scans with previous ones by making use of a process known as scan matching. This allows loop closures to be identified. When a loop closure is discovered it is then the SLAM algorithm utilizes this information to update its estimated robot trajectory.

The fact that the surroundings can change in time is another issue that can make it difficult to use SLAM. For instance, if a robot travels down an empty aisle at one point and then encounters stacks of pallets at the next point it will be unable to finding these two points on its map. This is where the handling of dynamics becomes critical and is a common characteristic of the modern Lidar SLAM algorithms.

Despite these challenges however, a properly designed SLAM system is extremely efficient for navigation and 3D scanning. It is especially beneficial in situations where the robot isn't able to depend on GNSS to determine its position, such as an indoor factory floor. It is important to keep in mind that even a properly-configured SLAM system can be prone to errors. To correct these errors it is essential to be able to recognize the effects of these errors and their implications on the SLAM process.

Mapping

The mapping function builds an outline of the robot's surrounding that includes the robot itself as well as its wheels and actuators, and everything else in its view. This map is used to aid in location, route planning, and obstacle detection. This is an area in which 3D lidars can be extremely useful since they can be used as the equivalent of a 3D camera (with one scan plane).

The process of building maps can take some time however, the end result pays off. The ability to build a complete, consistent map of the robot's environment allows it to conduct high-precision navigation, as being able to navigate around obstacles.

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

For this reason, there are a variety of different mapping algorithms that can be used with LiDAR sensors. One popular algorithm is called Cartographer which utilizes two-phase pose graph optimization technique to correct for drift and create a uniform global map. It is especially useful when used in conjunction with the odometry.

GraphSLAM is another option, that uses a set linear equations to model the constraints in the form of a diagram. The constraints are represented by an O matrix, as well as an X-vector. Each vertice of the O matrix represents a distance from the X-vector's landmark. A GraphSLAM update is an array of additions and subtraction operations on these matrix elements which means that all of the O and X vectors are updated to accommodate new observations of the robot.

SLAM+ is another useful mapping algorithm that combines odometry with 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 were recorded by the sensor. The mapping function can then utilize this information to improve its own position, allowing it to update the underlying map.

Obstacle Detection

A robot vacuum cleaner lidar needs to be able to perceive its environment to avoid obstacles and get to its destination. It employs sensors such as digital cameras, infrared scans sonar and LiDAR Robot Navigation laser radar to determine the surrounding. Additionally, it employs inertial sensors to measure its speed and position, as well as its orientation. These sensors allow it to navigate without danger and avoid collisions.

A range sensor is used to measure the distance between a robot and an obstacle. The sensor can be attached to the vehicle, the robot or even a pole. It is crucial to keep in mind that the sensor may be affected by various elements, including rain, wind, or fog. Therefore, it is crucial to calibrate the sensor prior to each use.

The results of the eight neighbor cell clustering algorithm can be used to detect static obstacles. This method isn't particularly accurate because of the occlusion induced by the distance between the laser lines and the camera's angular velocity. To address this issue, a technique of multi-frame fusion was developed to increase the detection accuracy of static obstacles.

The method of combining roadside camera-based obstacle detection with vehicle camera has been proven to increase data processing efficiency. It also reserves redundancy for other navigational tasks like planning a path. The result of this method is a high-quality image of the surrounding area that is more reliable than a single frame. The method has been compared with other obstacle detection methods including YOLOv5 VIDAR, YOLOv5, and monocular ranging, in outdoor comparison experiments.

The results of the test showed that the algorithm was able correctly identify the position and height of an obstacle, as well as its rotation and tilt. It also had a great performance in detecting the size of an obstacle and its color. The method was also reliable and reliable, even when obstacles were moving.