LiDAR Robot Navigation

LiDAR robots navigate using a combination of localization, mapping, as well as path planning. This article will outline the concepts and demonstrate how they work by using an example in which the robot reaches the desired goal within the space of a row of plants.

LiDAR sensors are low-power devices that can extend the battery life of a robot and reduce the amount of raw data needed for localization algorithms. This allows for a greater number of variations of the SLAM algorithm without overheating the GPU.

LiDAR Sensors

The sensor is the heart of Lidar systems. It emits laser beams into the environment. These light pulses strike objects and bounce back to the sensor at various angles, depending on the structure of the object. The sensor records the amount of time it takes to return each time and then uses it to calculate distances. Sensors are mounted on rotating platforms, which allows them to scan the area around them quickly and at high speeds (10000 samples per second).

LiDAR sensors are classified by their intended airborne or terrestrial application. Airborne lidars are typically attached to helicopters or unmanned aerial vehicle (UAV). Terrestrial LiDAR systems are usually mounted on a static robot platform.

To accurately measure distances, the sensor must always know the exact location of the robot. This information is usually gathered using a combination of inertial measuring units (IMUs), GPS, heavy Duty and time-keeping electronics. These sensors are employed by LiDAR systems to determine the precise position of the sensor within the space and time. The information gathered is used to create a 3D model of the surrounding.

LiDAR scanners can also be used to identify different surface types and types of surfaces, which is particularly useful for mapping environments with dense vegetation. When a pulse crosses a forest canopy, it is likely to register multiple returns. The first one is typically attributed to the tops of the trees, while the second one is attributed to the surface of the ground. If the sensor records each pulse as distinct, it is called discrete return LiDAR.

Distinte return scanning can be useful for studying the structure of surfaces. For instance, a forested area could yield the sequence of 1st 2nd and 3rd return, with a last large pulse representing the bare ground. The ability to separate these returns and store them as a point cloud allows for the creation of detailed terrain models.

Once a 3D model of the surrounding area is created, the robot can begin to navigate using this information. This involves localization as well as building a path that will take it to a specific 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 updates the path plan in line with the new obstacles.

SLAM Algorithms

SLAM (simultaneous mapping and localization) is an algorithm that allows your robot to map its environment and then identify its location relative to that map. Engineers use the information for a number of tasks, including the planning of routes and obstacle detection.

To enable SLAM to function the robot needs sensors (e.g. A computer that has the right software to process the data, Heavy Duty as well as a camera or a laser are required. Also, you need an inertial measurement unit (IMU) to provide basic positional information. The result is a system that can accurately track the location of your robot in a hazy environment.

The SLAM system is complicated and offers a myriad of back-end options. Whatever solution you select for your SLAM system, a successful SLAM system requires constant interaction between the range measurement device and the software that collects the data and the vehicle or robot itself. This is a dynamic process with almost infinite variability.

As the robot moves and around, it adds new scans to its map. The SLAM algorithm analyzes these scans against the previous ones using a process known as scan matching. This helps to establish loop closures. The SLAM algorithm updates its robot's estimated trajectory when a loop closure has been discovered.

Another issue that can hinder SLAM is the fact that the environment changes over time. For instance, if your robot is walking along an aisle that is empty at one point, but then comes across a pile of pallets at a different location, it may have difficulty connecting the two points on its map. This is when handling dynamics becomes important and is a common characteristic of modern lidar vacuum robot SLAM algorithms.

Despite these challenges however, a properly designed SLAM system is incredibly effective for navigation and 3D scanning. It is particularly beneficial in situations that don't depend on GNSS to determine its position, such as an indoor factory floor. However, it's important to keep in mind that even a properly configured SLAM system may have mistakes. To correct these errors it is crucial to be able detect the effects of these errors and their implications on the SLAM process.

Mapping

The mapping function creates a map of a robot's surroundings. This includes the robot vacuum with lidar and camera and its wheels, actuators, and everything else within its vision field. This map is used for localization, route planning and obstacle detection. This is an area in which 3D lidars are extremely helpful, as they can be utilized like the equivalent of a 3D camera (with only one scan plane).

Map building can be a lengthy process however, it is worth it in the end. The ability to create a complete, consistent map of the robot's surroundings allows it to conduct high-precision navigation, as as navigate around obstacles.

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

For this reason, there are a number of different mapping algorithms for use with LiDAR sensors. One of the most well-known algorithms is Cartographer which employs the two-phase pose graph optimization technique to adjust for drift and keep a consistent global map. It is particularly useful when combined with odometry.

GraphSLAM is a second option that uses a set linear equations to model the constraints in a diagram. The constraints are represented by an O matrix, and an the X-vector. Each vertice of the O matrix is a distance from an X-vector landmark. A GraphSLAM update consists of a series of additions and subtraction operations on these matrix elements which means that all of the X and O vectors are updated to accommodate new observations of 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 that were drawn by the sensor. The mapping function can then 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 makes use of sensors such as digital cameras, infrared scanners, sonar and laser radar to detect its environment. It also utilizes an inertial sensors to determine its position, speed and the direction. These sensors help it navigate in a safe way and avoid collisions.

One of the most important aspects of this process is the detection of obstacles that consists of the use of a range sensor to determine the distance between the robot and obstacles. The sensor can be positioned on the robot, inside the vehicle, or on poles. It is important to keep in mind that the sensor can be affected by a variety of factors, such as rain, wind, and fog. Therefore, it is essential to calibrate the sensor prior each use.

The most important aspect of obstacle detection is to identify static obstacles, which can be accomplished using the results of the eight-neighbor-cell clustering algorithm. However, this method has a low accuracy in detecting due to the occlusion created by the gap between the laser lines and the angular velocity of the camera making it difficult to recognize static obstacles in one frame. To address this issue multi-frame fusion was implemented to improve the accuracy of static obstacle detection.

The method of combining roadside unit-based and obstacle detection by a vehicle camera has been proven to improve the efficiency of processing data and reserve redundancy for further navigational operations, like path planning. The result of this method is a high-quality image of the surrounding area that is more reliable than one frame. The method has been compared against other obstacle detection methods, such as YOLOv5 VIDAR, Heavy Duty YOLOv5, and monocular ranging, in outdoor tests of comparison.

The results of the test proved that the algorithm could accurately determine the height and position of an obstacle as well as its tilt and rotation. It also had a good performance in detecting the size of the obstacle and its color. The method also showed good stability and robustness even when faced with moving obstacles.