LiDAR Robot Navigation

LiDAR robot navigation is a complex combination of mapping, localization and path planning. This article will outline the concepts and demonstrate how they work by using an easy example where the robot achieves a goal within a plant row.

LiDAR sensors are low-power devices that prolong the life of batteries on a robot and reduce the amount of raw data required for localization algorithms. This allows for a greater number of iterations of SLAM without overheating the GPU.

LiDAR Sensors

The sensor is the heart of Lidar systems. It releases laser pulses into the surrounding. These light pulses strike objects and bounce back to the sensor at a variety of angles, based on the composition of the object. The sensor is able to measure the time it takes to return each time, which is then used to calculate distances. The sensor is typically mounted 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 based on the type of sensor they are designed for applications in the air or on land. Airborne lidars are typically attached to helicopters or unmanned aerial vehicle (UAV). 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 gathered by a combination inertial measurement unit (IMU), GPS and time-keeping electronic. LiDAR systems use these 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 identify different surface types and types of surfaces, which is particularly useful for mapping environments with dense vegetation. When a pulse passes through a forest canopy, it is likely to register multiple returns. Usually, the first return is attributable to the top of the trees while the final return is attributed to the ground surface. If the sensor captures each peak of these pulses as distinct, this is called discrete return LiDAR.

The use of Discrete Return scanning can be helpful in studying the structure of surfaces. For instance, a forested region might yield a sequence of 1st, 2nd and 3rd returns with a last large pulse that represents the ground. The ability to separate and record these returns as a point cloud allows for precise terrain models.

Once an 3D map of the environment has been created and the robot is able to navigate based on this data. This process involves localization, creating an appropriate path to reach a navigation 'goal and dynamic obstacle detection. This is the process that detects new obstacles that were not present in the original map and updates the path plan in line with the new obstacles.

SLAM Algorithms

SLAM (simultaneous localization and mapping) is an algorithm that allows your robot to build a map of its environment and then determine where it is relative to the map. Engineers make use of this data for a variety of tasks, including planning a path and identifying obstacles.

To be able to use SLAM the robot vacuum cleaner lidar needs to be equipped with a sensor that can provide range data (e.g. A computer that has the right software to process the data and a camera or a laser are required. You also need an inertial measurement unit (IMU) to provide basic information on your location. The result is a system that will accurately determine the location of your robot in an unknown environment.

The SLAM system is complex and there are a variety of back-end options. Whatever solution you choose for a successful SLAM, it requires constant interaction between the range measurement device and the software that extracts the data and also the robot or LiDAR Robot Navigation vehicle. This is a highly dynamic procedure that can have an almost unlimited amount of variation.

As the robot moves and around, it adds new scans to its map. The SLAM algorithm analyzes these scans against the previous ones making use of a process known as scan matching. This allows loop closures to be identified. When a loop closure is discovered it is then the SLAM algorithm uses this information to update its estimate of the robot's trajectory.

The fact that the surrounding changes over time is another factor that complicates SLAM. For instance, if your robot walks through an empty aisle at one point and is then confronted by pallets at the next location, it will have difficulty matching these two points in its map. This is where the handling of dynamics becomes important and is a standard feature of modern Lidar SLAM algorithms.

SLAM systems are extremely efficient at navigation and 3D scanning despite these challenges. It is especially useful in environments that don't permit the robot to depend on GNSS for position, such as an indoor factory floor. However, it is important to remember that even a properly configured SLAM system can experience mistakes. It is essential to be able recognize these issues and comprehend how they affect the SLAM process to correct them.

Mapping

The mapping function creates a map for a robot's surroundings. This includes the robot, its wheels, actuators and everything else that is within its field of vision. The map is used to perform localization, path planning and obstacle detection. This is an area in which 3D lidars can be extremely useful because they can be effectively treated as the equivalent of a 3D camera (with only one scan plane).

Map creation 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 carry out high-precision navigation as well being able to navigate around obstacles.

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

For this reason, there are many different mapping algorithms for LiDAR robot navigation use with LiDAR sensors. Cartographer is a popular algorithm that employs a two-phase pose graph optimization technique. It corrects for drift while ensuring an unchanging global map. It is particularly efficient when combined with Odometry data.

Another alternative is GraphSLAM that employs linear equations to represent the constraints of a graph. The constraints are represented as an O matrix, and a vector X. Each vertice of 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.

Another helpful mapping algorithm is SLAM+, which combines mapping and odometry using an Extended Kalman Filter (EKF). The EKF updates the uncertainty of the robot's location as well as the uncertainty of the features drawn by the sensor. This information can be used 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 sense its surroundings to avoid obstacles and reach its final point. It makes use of sensors like digital cameras, infrared scans, sonar and laser radar to detect the environment. In addition, it uses inertial sensors to 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 an obstacle and a robot. The sensor can be attached to the vehicle, the robot, or a pole. It is important to remember that the sensor is affected by a variety of factors, including wind, rain and fog. Therefore, it is important to calibrate the sensor prior to every use.

The results of the eight neighbor cell clustering algorithm can be used to identify static obstacles. However this method is not very effective in detecting obstacles due to the occlusion caused by the spacing between different laser lines and the angle of the camera making it difficult to detect static obstacles within a single frame. To overcome this problem, multi-frame fusion was used to increase the accuracy of the static obstacle detection.

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

The experiment results proved that the algorithm could accurately identify the height and position of an obstacle, as well as its tilt and rotation. It was also able to identify the color and size of the object. The algorithm was also durable and reliable, even when obstacles were moving.