LiDAR Robot Navigation

LiDAR robot navigation is a complicated combination of localization, mapping, and path planning. This article will present these concepts and explain how they work together using an easy example of the robot achieving a goal within the middle of a row of crops.

LiDAR sensors are low-power devices which can extend the battery life of robots and reduce the amount of raw data required for localization algorithms. This allows for more iterations of SLAM without overheating the GPU.

LiDAR Sensors

The sensor is at the center of the Lidar system. It emits laser pulses into the environment. The light waves hit objects around and bounce back to the sensor at various angles, depending on the structure of the object. The sensor monitors the time it takes for each pulse to return and utilizes that information to determine distances. Sensors are mounted on rotating platforms, which allow them to scan the surroundings quickly and at high speeds (10000 samples per second).

LiDAR sensors are classified based on the type of sensor they're designed for, whether airborne application or terrestrial application. Airborne lidar systems are typically attached to helicopters, aircraft, or UAVs. (UAVs). Terrestrial LiDAR is usually mounted on a robot platform that is stationary.

To accurately measure distances the sensor must always know the exact location of the robot. This information is typically captured through a combination of inertial measuring units (IMUs), GPS, and time-keeping electronics. These sensors are used by LiDAR systems in order to determine the precise position of the sensor within the space and time. This information is used to build a 3D model of the surrounding.

LiDAR scanners can also be used to detect different types of surface, which is particularly useful for mapping environments with dense vegetation. When a pulse passes a forest canopy, it is likely to generate multiple returns. The first return is usually associated with the tops of the trees while the second one is attributed to the ground's surface. If the sensor captures these pulses separately and is referred to as discrete-return LiDAR.

Discrete return scanning can also be useful for studying the structure of surfaces. For instance forests can produce one or two 1st and 2nd returns, with the final big pulse representing the ground. The ability to separate and store these returns as a point-cloud allows for precise models of terrain.

Once a 3D model of the surrounding area has been created, the robot can begin to navigate based on this data. This process involves localization, creating the path needed to reach a goal for navigation,' and dynamic obstacle detection. The latter is the method of identifying new obstacles that are not present in 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 environment, and then determine its location relative to that map. Engineers make use of this information to perform a variety of tasks, such as planning a path and identifying obstacles.

To be able to use SLAM your robot has to be equipped with a sensor that can provide range data (e.g. A computer with the appropriate software to process the data and a camera or a laser are required. Also, you need an inertial measurement unit (IMU) to provide basic information on your location. The result is a system that can accurately determine the location of your robot in an unspecified environment.

The SLAM system is complex and there are many different back-end options. No matter which one you choose for your SLAM system, a successful SLAM system requires a constant interaction between the range measurement device and the software that collects the data and the vehicle or robot. It is a dynamic process with almost infinite variability.

As the robot moves it adds scans to its map. The SLAM algorithm compares these scans to prior ones using a process known as scan matching. This assists in establishing loop closures. The SLAM algorithm adjusts its estimated robot trajectory once loop closures are identified.

The fact that the surroundings changes in time is another issue that complicates SLAM. For instance, if a robot is walking down an empty aisle at one point, and is then confronted by pallets at the next spot it will be unable to matching these two points in its map. This is where the handling of dynamics becomes crucial, and this is a common feature of the modern Lidar SLAM algorithms.

Despite these issues, a properly configured SLAM system is extremely efficient for navigation and 3D scanning. It is especially beneficial in environments that don't permit the robot to depend on GNSS for positioning, such as an indoor factory floor. However, it's important to keep in mind that even a well-configured SLAM system can be prone to errors. It is crucial to be able to detect these flaws and understand how they impact the SLAM process to fix them.

Mapping

The mapping function creates a map for a robot's environment. This includes the robot and its wheels, actuators, and everything else that is within its vision field. The 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 utilized as the equivalent of a 3D camera (with a single scan plane).

Map building is a long-winded process but it pays off in the end. The ability to create a complete and coherent map of the robot's surroundings allows it to navigate with high precision, as well as around obstacles.

As a general rule of thumb, the greater resolution of the sensor, the 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 an industrial robotics system navigating large factories.

There are many different mapping algorithms that can be employed with LiDAR sensors. One of the most well-known algorithms is Cartographer which employs two-phase pose graph optimization technique to correct for drift and maintain a consistent global map. It is particularly beneficial when used in conjunction with Odometry data.

GraphSLAM is another option, that uses a set linear equations to represent constraints in a diagram. The constraints are represented by an O matrix, and an the X-vector. Each vertice of the O matrix represents a distance from the X-vector's landmark. A GraphSLAM update consists of a series of additions and subtraction operations on these matrix elements, and the result is that all of the O and X vectors are updated to account for new observations of the robot.

SLAM+ is another useful mapping algorithm that combines odometry and mapping using an Extended Kalman filter (EKF). The EKF updates not only the uncertainty in the robot's current location, but also the uncertainty in the features that were recorded by the sensor. The mapping function is able to utilize this information to better estimate its own position, which allows it to update the underlying map.

Obstacle Detection

A robot needs to be able to see its surroundings in order to avoid obstacles and reach its goal point. It utilizes sensors such as digital cameras, infrared scanners laser radar and sonar to sense its surroundings. In addition, it uses inertial sensors that measure its speed and position, Lidar robot navigation as well as its orientation. These sensors allow it to navigate safely and avoid collisions.

A range sensor is used to determine the distance between a robot and an obstacle. The sensor can be mounted on the robot, inside an automobile or on the pole. It is crucial to keep in mind that the sensor could be affected by a variety of factors, such as rain, wind, or fog. It is crucial to calibrate the sensors prior each use.

The results of the eight neighbor cell clustering algorithm can be used to identify static obstacles. However, this method has a low accuracy in detecting due to the occlusion created by the gap between the laser lines and the angle of the camera, which makes it difficult to detect static obstacles in a single frame. To overcome this problem, a technique of multi-frame fusion was developed to increase the accuracy of detection of static obstacles.

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

The results of the test proved that the algorithm could accurately identify the height and location of an obstacle as well as its tilt and rotation. It was also able detect the size and color of an object. The method also exhibited good stability and robustness, even in the presence of moving obstacles.