The Reasons Lidar Robot Navigation Isn't As Easy As You Imagine

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 an easy example where the robot reaches an objective within the space of a row of plants.

LiDAR sensors are low-power devices that can prolong the battery life of robots 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 core of a lidar system is its sensor which emits laser light pulses into the environment. The light waves hit objects around and bounce back to the sensor at various angles, based on the composition of the object. The sensor records the amount of time required to return each time and then uses it to calculate distances. Sensors are placed on rotating platforms that allow them to scan the surroundings quickly and at high speeds (10000 samples per second).

LiDAR sensors are classified based on whether they're intended for use in the air or on the ground. Airborne lidars are typically mounted on helicopters or an unmanned aerial vehicles (UAV). Terrestrial LiDAR is usually mounted 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 gathered by a combination of an inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems use sensors to compute the precise location of the sensor in time and space, which is then used to build up a 3D map of the surrounding area.

LiDAR scanners are also able to recognize different types of surfaces, which is particularly useful when mapping environments that have dense vegetation. For LiDAR robot navigation instance, when an incoming pulse is reflected through a canopy of trees, it is likely to register multiple returns. The first one is typically associated with the tops of the trees while the second is associated with the ground's surface. If the sensor records these pulses separately this is known as discrete-return LiDAR.

Distinte return scans can be used to determine surface structure. For instance, a forested area could yield a sequence of 1st, 2nd and 3rd returns with a final, large pulse that represents the ground. The ability to separate these returns and store them as a point cloud makes it possible to create detailed terrain models.

Once a 3D model of the environment is created, the robot can begin to navigate based on this data. This involves localization and creating a path to get to a navigation "goal." It also involves dynamic obstacle detection. This is the process that 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 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 utilize the data for a variety of tasks, including the planning of routes and obstacle detection.

To use SLAM your robot has to have a sensor that provides range data (e.g. the laser or camera), and a computer that has the right software to process the data. You'll also require an IMU to provide basic positioning information. The result is a system that will accurately determine the location of your robot in an unknown environment.

The SLAM process is a complex one and a variety of back-end solutions are available. Whatever solution you choose for the success of SLAM, it requires a constant interaction between the range measurement device and the software that extracts the data and the robot or vehicle. This is a dynamic procedure with a virtually unlimited variability.

As the robot moves, it adds scans to its map. The SLAM algorithm then compares these scans with the previous ones using a method called scan matching. This allows loop closures to be established. The SLAM algorithm adjusts its estimated robot trajectory when the loop has been closed discovered.

Another issue that can hinder SLAM is the fact that the surrounding changes as time passes. If, for example, your robot is walking down an aisle that is empty at one point, but then encounters a stack of pallets at a different point, it may have difficulty matching the two points on its map. This is where the handling of dynamics becomes crucial, and this is a standard feature of modern Lidar SLAM algorithms.

Despite these issues, a properly configured SLAM system is extremely efficient for navigation and 3D scanning. It is particularly beneficial in situations where the robot isn't able to rely on GNSS for its positioning for example, an indoor factory floor. It is crucial to keep in mind that even a well-designed SLAM system can be prone to errors. To correct these mistakes, it is important to be able to recognize them and comprehend their impact on the SLAM process.

Mapping

The mapping function creates a map for a robot's surroundings. This includes the robot, its wheels, actuators and everything else that falls within its vision field. This map is used for localization, route planning and obstacle detection. This is a domain in which 3D Lidars are particularly useful, since they can be treated as an 3D Camera (with a single scanning plane).

The map building process can take some time however the results pay off. The ability to create a complete and consistent map of a robot's environment allows it to navigate with great precision, as well as over obstacles.

As a rule of thumb, the higher resolution the sensor, more precise the map will be. Not all robots require maps with high resolution. For example a floor-sweeping robot might not require the same level detail as a robotic system for industrial use that is navigating factories of a large size.

This is why there are a number of different mapping algorithms that can be used with lidar vacuum sensors. Cartographer is a popular algorithm that employs a two-phase pose graph optimization technique. It corrects for drift while maintaining an unchanging global map. It is particularly useful when paired with Odometry data.

Another alternative is GraphSLAM, which uses a system of linear equations to model the constraints of graph. The constraints are represented by an O matrix, and an the X-vector. Each vertice in the O matrix is the distance to an X-vector landmark. A GraphSLAM Update is a series subtractions and additions to these matrix elements. The result is that all the O and X vectors are updated to reflect the latest observations made by 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 of the robot's current location, 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 position and update the map.

Obstacle Detection

A robot needs to be able to perceive its environment so that it can avoid obstacles and get to its goal. It utilizes 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, position and orientation. These sensors help it navigate safely 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 keep in mind that the sensor can be affected by a variety of factors, including wind, rain and fog. Therefore, it is important to calibrate the sensor prior to each use.

A crucial step in obstacle detection is the identification of static obstacles. This can be done 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 overcome this problem multi-frame fusion was employed to increase the accuracy of static obstacle detection.

The method of combining roadside unit-based as well as vehicle camera obstacle detection has been shown to improve the efficiency of data processing and reserve redundancy for subsequent navigation operations, such as path planning. The result of this method is a high-quality picture of the surrounding environment that is more reliable than one frame. In outdoor comparison experiments the method was compared against other methods for detecting obstacles such as YOLOv5 monocular ranging, and VIDAR.

The results of the experiment proved that the algorithm could accurately determine the height and location of an obstacle, as well as its tilt and rotation. It was also able detect the color and size of the object. The method was also robust and stable even when obstacles were moving.