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LiDAR Robot Navigation

LiDAR robot navigation is a sophisticated combination of mapping, localization and path planning. This article will explain the concepts and demonstrate how they work using a simple example where the robot achieves a goal within a row of plants.

LiDAR sensors have modest power requirements, allowing them to increase a robot's battery life and decrease the amount of raw data required for localization algorithms. This enables more variations of the SLAM algorithm without overheating the GPU.

LiDAR Sensors

The sensor is the core of Lidar systems. It releases laser pulses into the environment. The light waves hit objects around and bounce back to the sensor at a variety of angles, depending on the structure of the object. The sensor measures the amount of time required to return each time and then uses it to calculate distances. The sensor is usually placed on a rotating platform allowing it to quickly scan the entire area at high speed (up to 10000 samples per second).

LiDAR sensors are classified according to their intended applications on land or in the air. Airborne lidar systems are usually connected to aircrafts, helicopters or unmanned aerial vehicles (UAVs). Terrestrial LiDAR is usually installed on a robot platform that is stationary.

To accurately measure distances, the sensor must know the exact position of the robot at all times. This information is captured using a combination of inertial measurement unit (IMU), GPS and LiDAR robot navigation time-keeping electronic. These sensors are employed by lidar robot vacuum and mop systems to determine the exact location of the sensor in the space and time. This information is then used to create a 3D representation of the environment.

LiDAR scanners are also able to identify various types of surfaces which is especially useful when mapping environments with dense vegetation. For instance, when a pulse passes through a forest canopy it is common for it to register multiple returns. The first return is usually attributed to the tops of the trees, while the second is associated with the ground's surface. If the sensor captures these pulses separately, it is called discrete-return LiDAR.

Distinte return scans can be used to analyze the structure of surfaces. For instance, a forest area could yield an array of 1st, 2nd, and 3rd returns, with a final large pulse representing the ground. The ability to separate and store these returns as a point cloud allows for LiDAR robot navigation precise models of terrain.

Once a 3D model of environment is constructed, 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 are not listed in the map that was created and then updates the plan of travel in line with the new obstacles.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its surroundings and then determine its position in relation to the map. Engineers make use of this information for a range of tasks, such as the planning of routes and obstacle detection.

To be able to use SLAM the robot needs to be equipped with a sensor that can provide range data (e.g. A computer with the appropriate software to process the data as well as cameras or lasers are required. Also, you need an inertial measurement unit (IMU) to provide basic positional information. The result is a system that can precisely track the position of your robot in an unspecified environment.

The SLAM system is complex and offers a myriad of back-end options. No matter which one 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 dynamic procedure that is almost indestructible.

As the robot moves around, it adds new scans to its map. The SLAM algorithm then compares these scans with earlier ones using a process called scan matching. This allows loop closures to be identified. The SLAM algorithm adjusts its estimated robot trajectory once the loop has been closed detected.

The fact that the surroundings can change over time is a further factor that makes it more difficult for SLAM. If, for example, your robot is walking down an aisle that is empty at one point, but it comes across a stack of pallets at a different location, it may have difficulty matching the two points on its map. This is where handling dynamics becomes crucial, and this is a typical feature of modern Lidar SLAM algorithms.

SLAM systems are extremely efficient at navigation and 3D scanning despite these limitations. It is especially beneficial in environments that don't permit the robot to rely on GNSS positioning, like an indoor factory floor. It is crucial to keep in mind that even a properly configured SLAM system may experience errors. To correct these errors, it is important to be able to recognize them and understand their impact on the SLAM process.

Mapping

The mapping function creates an outline of the robot's surroundings which includes the robot as well as its wheels and actuators, and everything else in the area of view. This map is used for the localization of the robot, route planning and obstacle detection. This is an area where 3D lidars can be extremely useful, as they can be utilized like an actual 3D camera (with one scan plane).

The process of creating maps may take a while however the results pay off. The ability to build an accurate, complete map of the robot's surroundings allows it to perform high-precision navigation, as well being able to navigate around obstacles.

As a rule, the higher the resolution of the sensor, then the more accurate will be the map. Not all robots require maps with high resolution. For example a floor-sweeping robot may not require the same level of detail as an industrial robotic system navigating large factories.

There are many different mapping algorithms that can be utilized with LiDAR sensors. Cartographer is a well-known algorithm that utilizes the two-phase pose graph optimization technique. It adjusts for drift while maintaining an accurate global map. It is especially beneficial when used in conjunction with odometry data.

Another option is GraphSLAM that employs a system of linear equations to model constraints of graph. The constraints are represented as an O matrix and an X vector, with each vertex of the O matrix containing the distance to a point on the X vector. A GraphSLAM update is an array of additions and subtraction operations on these matrix elements which means that all of the X and O vectors are updated to reflect new robot observations.

Another helpful mapping algorithm is SLAM+, which combines the use of odometry with mapping using an Extended Kalman Filter (EKF). The EKF updates not only the uncertainty of the robot's current position but also the uncertainty in 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 must be able perceive its environment so that it can avoid obstacles and reach its goal. It makes use of sensors such as digital cameras, infrared scanners laser radar and sonar to detect its environment. In addition, it uses inertial sensors that measure its speed and position as well as its orientation. These sensors help it navigate in a safe manner and avoid collisions.

A range sensor is used to measure the distance between a robot and an obstacle. The sensor can be mounted to the vehicle, the robot, or a pole. It is important to remember that the sensor can be affected by a myriad of factors like rain, wind and fog. It is important to calibrate the sensors prior to every use.

A crucial step in obstacle detection is the identification of static obstacles, which can be accomplished using the results of the eight-neighbor-cell clustering algorithm. This method is not very accurate because of the occlusion created by the distance between the laser lines and the camera's angular speed. To overcome this problem multi-frame fusion was implemented to improve the accuracy of static obstacle detection.

The technique of combining roadside camera-based obstruction detection with the vehicle camera has been proven to increase the efficiency of data processing. It also reserves the possibility of redundancy for other navigational operations like planning a path. This method creates a high-quality, reliable image of the surrounding. In outdoor comparison experiments, the method was compared to other methods of obstacle detection such as YOLOv5, monocular ranging and VIDAR.

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