LiDAR Robot Navigation

LiDAR robots move using a combination of localization, mapping, as well as path planning. This article will present these concepts and show how they interact using an example of a robot achieving its goal in a row of crop.

Lidar Navigation sensors are low-power devices which can prolong the life of batteries on robots and decrease the amount of raw data required to run localization algorithms. This allows for more variations of the SLAM algorithm without overheating the GPU.

LiDAR Sensors

The sensor is the heart of a Lidar system. It emits laser beams into the surrounding. These pulses bounce off objects around them at different angles depending on their composition. The sensor measures the amount of time it takes to return each time and uses this information to determine distances. The sensor is typically mounted on a rotating platform, permitting it to scan the entire area at high speeds (up to 10000 samples per second).

LiDAR sensors are classified based on their intended airborne or terrestrial application. Airborne lidar systems are typically connected to aircrafts, helicopters, or UAVs. (UAVs). Terrestrial LiDAR systems are usually mounted on a stationary robot platform.

To accurately measure distances, the sensor must always know the exact location of the robot. This information is recorded by a combination of an inertial measurement unit (IMU), GPS and time-keeping electronic. These sensors are utilized by LiDAR systems to determine the exact location of the sensor within space and time. This information is then used to create a 3D model of the surrounding.

LiDAR scanners are also able to identify different kinds of surfaces, which is particularly beneficial when mapping environments with dense vegetation. When a pulse passes a forest canopy it will usually produce multiple returns. The first return is attributed to the top of the trees, and the last one is attributed to the ground surface. If the sensor records these pulses in a separate way, it is called discrete-return LiDAR.

Discrete return scanning can also be useful for analyzing the structure of surfaces. For instance the forest may yield one or two 1st and 2nd returns, with the final large pulse representing bare 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 equipped to navigate. This process involves localization, constructing a path to reach a navigation 'goal,' and dynamic obstacle detection. This is the process that detects new obstacles that are not listed in the map's original version and updates 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 position of the robot relative to the map. Engineers use this information for a range of tasks, including path planning and obstacle detection.

To enable SLAM to function the robot needs sensors (e.g. A computer that has the right software for processing the data and cameras or lasers are required. You will also require an inertial measurement unit (IMU) to provide basic positional information. The system will be able to track your robot's location accurately in an undefined environment.

The SLAM system is complex and there are a variety of back-end options. Regardless of which solution you choose the most effective SLAM system requires a constant interaction between the range measurement device and the software that extracts the data and the vehicle or robot itself. It is a dynamic process with a virtually unlimited variability.

As the robot moves about the area, it adds new scans to its map. The SLAM algorithm compares these scans to prior ones using a process called scan matching. This allows loop closures to be identified. When a loop closure has been detected it is then the SLAM algorithm utilizes this information to update its estimated robot trajectory.

Another factor that makes SLAM is the fact that the scene changes in time. For instance, if your robot is navigating an aisle that is empty at one point, but then comes across a pile of pallets at a different point it might have trouble finding the two points on its map. Handling dynamics are important in this case and are a feature of many modern Lidar SLAM algorithms.

Despite these difficulties, a properly configured SLAM system can be extremely effective for navigation and 3D scanning. It is particularly beneficial in situations where the robot can't rely on GNSS for positioning, such as an indoor factory floor. It is important to keep in mind that even a well-designed SLAM system could be affected by mistakes. To fix these issues, Lidar navigation it is important to be able to recognize them and understand their impact on the SLAM process.

Mapping

The mapping function builds a map of the robot's environment, which includes the robot itself including its wheels and actuators, and everything else in the area of view. This map is used for location, route planning, and obstacle detection. This is an area in which 3D Lidars can be extremely useful as they can be regarded as a 3D Camera (with a single scanning plane).

Map building is a time-consuming process, but it pays off in the end. The ability to build a complete and consistent map of the environment around a robot allows it to navigate with great precision, as well as 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 example floor sweepers might not require the same level of detail as an industrial robotic system navigating large factories.

This is why there are a number of different mapping algorithms for LiDAR navigation use with LiDAR sensors. Cartographer is a well-known algorithm that utilizes a two phase pose graph optimization technique. It corrects for drift while ensuring an accurate global map. It is particularly useful when paired with odometry.

GraphSLAM is another option, that uses a set linear equations to represent the constraints in diagrams. The constraints are modeled as an O matrix and an the X vector, with every vertex of the O matrix containing the distance to a landmark on the X vector. A GraphSLAM Update is a series of additions and subtractions on these matrix elements. The result is that all O and X Vectors are updated to account for the new observations made by the robot.

Another efficient mapping algorithm is SLAM+, which combines odometry and mapping using an Extended Kalman Filter (EKF). The EKF alters the uncertainty of the robot's position as well as the uncertainty of the features mapped by the sensor. This information can be used by the mapping function to improve its own estimation of its position and update the map.

Obstacle Detection

A robot needs to be able to sense its surroundings to avoid obstacles and get to its desired point. It makes use of sensors like digital cameras, infrared scans sonar and laser radar to determine the surrounding. It also utilizes an inertial sensors to monitor its speed, position and orientation. These sensors assist it in navigating in a safe and secure manner and avoid collisions.

A range sensor is used to gauge the distance between an obstacle and a robot vacuum with lidar. The sensor can be positioned on the robot, in an automobile or on the pole. It is crucial to keep in mind that the sensor can be affected by a variety of elements, including wind, rain and fog. Therefore, it is essential to calibrate the sensor prior to each use.

An important step in obstacle detection is to identify 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 caused by the spacing between different laser lines and the angle of the camera, which makes it difficult to identify static obstacles within a single frame. To address this issue multi-frame fusion was employed to improve the accuracy of the static obstacle detection.

The technique of combining roadside camera-based obstacle detection with a vehicle camera has been proven to increase the efficiency of processing data. It also reserves redundancy for other navigational tasks like planning a path. This method produces a high-quality, reliable image of the environment. The method has been tested with other obstacle detection techniques including YOLOv5, VIDAR, and monocular ranging in outdoor comparison experiments.

The experiment results revealed that the algorithm was able to accurately identify the height and location of obstacles as well as its tilt and rotation. It was also able detect the size and color of an object. The method also demonstrated excellent stability and durability even when faced with moving obstacles.