LiDAR Robot Navigation

LiDAR robots move using a combination of localization, mapping, as well as path planning. This article will introduce the concepts and demonstrate how they function using an example in which the robot reaches the desired goal within the space of a row of plants.

LiDAR sensors have low power requirements, which allows them to prolong the life of a robot vacuum with lidar and camera's battery and decrease the amount of raw data required for localization algorithms. This allows for more iterations of the SLAM algorithm without overheating the GPU.

LiDAR Sensors

The central component of lidar systems is its sensor which emits laser light in the surrounding. These light pulses bounce off the surrounding objects at different angles based on their composition. The sensor measures the time it takes to return each time and uses this information to determine distances. The sensor is usually placed on a rotating platform, allowing it to quickly scan the entire surrounding area at high speed (up to 10000 samples per second).

LiDAR sensors are classified based on whether they are designed for applications in the air or on land. Airborne lidar systems are typically attached to helicopters, aircraft, or UAVs. (UAVs). Terrestrial LiDAR systems are generally mounted on a static 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 measuring units (IMUs), GPS, and time-keeping electronics. These sensors are employed by LiDAR systems to calculate the precise 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 recognize different types of surfaces, which is particularly useful when mapping environments that have dense vegetation. When a pulse passes a forest canopy it will usually produce multiple returns. The first return is usually attributable to the tops of the trees while the second is associated with the ground's surface. If the sensor captures each pulse as distinct, LiDAR Robot Navigation it is known as discrete return LiDAR.

Distinte return scans can be used to analyze the structure of surfaces. For example, a forest region may produce one or two 1st and 2nd returns with the last one representing the ground. The ability to separate and record these returns as a point-cloud allows for precise models of terrain.

Once a 3D map of the surroundings is created and the robot is able to navigate using this information. This process involves localization and creating a path to get to a navigation "goal." It also involves dynamic obstacle detection. This process identifies new obstacles not included in the map that was created and then updates the plan of travel according to the new obstacles.

SLAM Algorithms

SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to create an outline of its surroundings and then determine where it is relative to the map. Engineers use this information to perform a variety of tasks, such as path planning and obstacle detection.

To allow SLAM to function, your robot must have sensors (e.g. laser or camera), and a computer with the appropriate software to process the data. You will also need an IMU to provide basic information about your position. The system will be able to track your robot's exact location in an undefined environment.

The SLAM process is complex and many back-end solutions are available. Whatever solution you select for your SLAM system, a successful SLAM system requires constant interaction between the range measurement device and the software that extracts the data, and the robot or vehicle itself. This is a highly dynamic procedure that can have an almost infinite amount of variability.

As the robot moves about the area, LiDAR robot navigation it adds new scans to its map. The SLAM algorithm then compares these scans to the previous ones using a method called scan matching. This helps to establish loop closures. The SLAM algorithm is updated with its estimated robot trajectory when a loop closure has been identified.

The fact that the environment changes over time is another factor that complicates SLAM. For instance, if your robot is walking down an empty aisle at one point, and then encounters stacks of pallets at the next spot it will be unable to connecting these two points in its map. This is when handling dynamics becomes crucial and is a typical feature of the modern lidar robot vacuums SLAM algorithms.

SLAM systems are extremely efficient in navigation and 3D scanning despite these limitations. It is especially useful in environments where the robot isn't able to rely on GNSS for positioning, such as an indoor factory floor. It's important to remember that even a properly-configured SLAM system can be prone to errors. To fix these issues it is essential to be able to spot them and understand their impact on the SLAM process.

Mapping

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

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

The higher the resolution of the sensor then 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 an industrial robotics system navigating large factories.

There are many different mapping algorithms that can be used with LiDAR sensors. Cartographer is a very popular 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 the odometry information.

GraphSLAM is another option, that uses a set linear equations to represent the constraints in a diagram. The constraints are modelled as an O matrix and an X vector, with each vertice of the O matrix containing a distance to a landmark on the X vector. A GraphSLAM update consists of a series of additions and subtraction operations on these matrix elements with the end result being that all of the X and O vectors are updated to accommodate new information about the robot.

SLAM+ is another useful mapping algorithm that combines odometry with 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 recorded by the sensor. The mapping function is able to make use of this information to improve its own position, allowing it to update the base map.

Obstacle Detection

A robot must be able see its surroundings to avoid obstacles and reach its destination. It utilizes sensors such as digital cameras, infrared scanners laser radar and sonar to detect its environment. It also makes use of an inertial sensor to measure its position, speed and its orientation. These sensors help it navigate in a safe way and avoid collisions.

One important part of this process is the detection of obstacles, which involves the use of an IR range sensor to measure the distance between the robot and the obstacles. The sensor can be mounted to the robot, a vehicle or even a pole. It is important to remember that the sensor could be affected by a variety of elements, including wind, rain, and fog. Therefore, it is essential to calibrate the sensor prior each use.

A crucial step in obstacle detection is the identification of static obstacles, which can be accomplished by using the results of the eight-neighbor-cell clustering algorithm. This method is not very precise due to the occlusion induced by the distance between the laser lines and the camera's angular velocity. To address this issue multi-frame fusion was employed to increase the accuracy of the static obstacle detection.

The technique of combining roadside camera-based obstacle detection with the vehicle camera has proven to increase the efficiency of data processing. It also provides redundancy for other navigation operations like the planning of a path. The result of this method is a high-quality picture of the surrounding area that is more reliable than a single frame. In outdoor tests the method was compared against other obstacle detection methods such as YOLOv5 monocular ranging, and VIDAR.

The results of the study revealed that the algorithm was able correctly identify the location and height of an obstacle, in addition to its rotation and tilt. It was also able to identify the color and size of an object. The algorithm was also durable and steady even when obstacles moved.