LiDAR Robot Navigation

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

LiDAR sensors are relatively low power requirements, which allows them to prolong the battery life of a robot and decrease the need for raw data for localization algorithms. This enables more iterations of the SLAM algorithm without overheating the GPU.

LiDAR Sensors

The sensor is at the center of a Lidar system. It emits laser pulses into the surrounding. The light waves hit objects around and bounce back to the sensor at various angles, based on the composition of the object. The sensor determines how long it takes for each pulse to return and then utilizes that information to determine distances. The sensor is typically placed on a rotating platform which allows it to scan the entire surrounding area at high speeds (up to 10000 samples per second).

LiDAR sensors are classified based on their intended applications on land or in the air. Airborne lidar systems are usually connected to aircrafts, helicopters or UAVs. (UAVs). Terrestrial LiDAR systems are usually placed on a stationary robot platform.

To accurately measure distances, the sensor must always know the exact location of the robot. This information is usually gathered by an array of inertial measurement units (IMUs), GPS, and time-keeping electronics. These sensors are utilized by lidar vacuum robot systems in order to determine the precise location of the sensor in the space and time. This information is then used to build a 3D model of the surrounding.

LiDAR scanners can also detect different types of surfaces, which is especially useful when mapping environments that have dense vegetation. When a pulse passes through a forest canopy it will usually register multiple returns. Usually, the first return is attributed to the top of the trees, while the final return is associated with the ground surface. If the sensor captures each peak of these pulses as distinct, it is known as discrete return LiDAR.

Discrete return scanning can also be useful for studying surface structure. For instance, a forest region could produce the sequence of 1st 2nd and 3rd returns with a last large pulse representing the bare ground. The ability to separate these returns and store them as a point cloud allows to create detailed terrain models.

Once an 3D model of the environment is built, the robot will be capable of using this information to navigate. This involves localization and making a path that will get to a navigation "goal." It also involves dynamic obstacle detection. This is the method of identifying new obstacles that are not present on the original map and adjusting the path plan in line with the new obstacles.

SLAM Algorithms

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

For SLAM to function, your robot must have sensors (e.g. laser or camera), and a computer with the right software to process the data. You will also require an inertial measurement unit (IMU) to provide basic positional information. The system can determine your robot's location accurately in an unknown environment.

The SLAM process is extremely complex and many back-end solutions exist. No matter which solution you choose to implement an effective SLAM is that it requires a constant interaction between the range measurement device and the software that collects data and the vehicle or robot. 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 with previous ones by making use of a process known as scan matching. This aids in establishing loop closures. The SLAM algorithm adjusts its robot vacuums with lidar's estimated trajectory when loop closures are detected.

The fact that the surrounding changes in time is another issue that makes it more difficult for SLAM. For instance, if your robot is walking down an aisle that is empty at one point, and 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 scenario and are a feature of many modern Lidar SLAM algorithms.

Despite these issues, a properly-designed SLAM system can be extremely effective for navigation and 3D scanning. It is especially beneficial in environments that don't let the robot rely on GNSS-based positioning, such as an indoor factory floor. However, it's important to note that even a properly configured SLAM system may have mistakes. To fix these issues it is essential to be able detect them and understand their impact on the SLAM process.

Mapping

The mapping function builds a map of the robot's surroundings which includes the robot itself as well as its wheels and actuators and everything else that is in the area of view. The map is used to perform localization, path planning, and obstacle detection. This is an area in which 3D Lidars are particularly useful because they can be used as an 3D Camera (with one scanning plane).

Map building is a long-winded process but it pays off in the end. The ability to create a complete, consistent map of the surrounding area allows it to perform high-precision navigation, as well being able to navigate around obstacles.

As a rule, the greater the resolution of the sensor, then the more accurate will be the map. Not all robots require high-resolution maps. For instance floor sweepers might not require the same level of detail as an industrial robotics system operating in large factories.

There are a variety of mapping algorithms that can be used with LiDAR sensors. Cartographer is a very popular algorithm that uses a two phase pose graph optimization technique. It corrects for drift while ensuring an accurate global map. It is particularly effective when used in conjunction with the odometry.

GraphSLAM is a different option, which uses a set of linear equations to represent the constraints in diagrams. The constraints are modeled as an O matrix and a one-dimensional X vector, each vertex of the O matrix representing the distance to a landmark on the X vector. A GraphSLAM Update is a sequence of additions and subtractions on these matrix elements. The end result is that both the O and X Vectors are updated to take into account the latest observations made by the robot.

Another efficient mapping algorithm is SLAM+, which combines odometry and mapping using an Extended Kalman filter (EKF). The EKF changes the uncertainty of the robot's position as well as the uncertainty of the features that were recorded 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 needs to be able to see its surroundings so it can avoid obstacles and reach its final point. It makes use of sensors like digital cameras, infrared scans, laser radar, and cleaner sonar to determine the surrounding. It also uses inertial sensors to monitor its position, speed and the direction. These sensors allow it to navigate safely and avoid collisions.

A range sensor is used to gauge the distance between an obstacle and a robot. The sensor can be positioned on the robot, cleaner in the vehicle, or on poles. It is crucial to remember that the sensor can be affected by a variety of elements such as wind, rain and fog. It is essential to calibrate the sensors prior each use.

The results of the eight neighbor cell clustering algorithm can be used to detect static obstacles. This method is not very accurate because of the occlusion induced by the distance between laser lines and the camera's angular velocity. To address this issue, a method of multi-frame fusion has been used to improve the detection accuracy of static obstacles.

The method of combining roadside unit-based as well as vehicle camera obstacle detection has been proven to increase the data processing efficiency and reserve redundancy for further navigational tasks, like path planning. This method provides an image of high-quality and reliable of the environment. In outdoor tests, the method was compared to other methods of obstacle detection such as YOLOv5, monocular ranging and VIDAR.

The results of the experiment showed that the algorithm could correctly identify the height and location of obstacles as well as its tilt and rotation. It also had a great ability to determine the size of the obstacle and its color. The method also exhibited solid stability and reliability even in the presence of moving obstacles.