The secret of robot autonomous movement, starting from SLAM technology

Intelligent service robots are becoming the cusp of the industry. Starting from cleaning robots, family companion robots and food delivery robots have entered the public eye. When discussing whether such robots can solve practical problems, autonomous positioning navigation technology as the first step of robot intelligence is constantly attracting attention in the industry. At the same time, as an important breakthrough in autonomous positioning navigation technology, SLAM technology has also become the focus of attention.

As shown in the figure, the robot autonomous positioning navigation technology includes positioning, map creation and path planning (motion control), while SLAM itself only completes the positioning of the robot and map creation, which are different.

So, how is SLAM technology implemented? What are the difficulties? How does the robot implement path planning and automatic navigation? What kind of sweeping robot is smart?

These questions, Xiaobian will answer you one by one, leading everyone to understand the secret of robotic autonomous movement.

SLAM is an abbreviation for Simultaneous LocalizaTIon And Mapping, first proposed by Hugh Durrant-Whyte and John J. Leonard.

In fact, SLAM is more like a concept than an algorithm. It itself contains many steps, each of which can be implemented using different algorithms. It is mainly used to solve the problem of real-time positioning and map construction when mobile robots are running in an unknown environment.

When you are in a different place, how can you find exactly where you want to go? How do you find a way home when you get lost outdoors? Yes, we have navigation software and outdoor maps.

Like human mapping, the process of robots describing the environment and understanding the environment is mainly relying on maps. It uses an environmental map to describe its current environmental information and uses different map descriptions as the algorithm and sensor used.

There are four ways to represent maps in robotics: raster maps, feature maps, direct representations, and topological maps.

1, raster map

The most common way for a robot to describe an environment map is a Gridmap or OccupancyMap. A raster map divides the environment into a series of rasters, each of which gives a possible value, indicating the probability that the raster is occupied.

This map looks no different from the maps that people recognize. It was first proposed by NASA's AlbertoElfes in 1989 and used on Mars rover. The essence is a bitmap image, but each of them is " "Pixels" means the probability distribution of obstacles in the actual environment.

In general, the map can be used when a sensor that can directly measure distance data such as a laser radar, a depth camera, an ultrasonic sensor, or the like is used for SLAM. This map can also be drawn by distance measuring sensors, ultrasound (early), and lidar (now).

2. Feature point map

The feature point map is an environment that uses related geometric features (such as points, lines, and faces), and is commonly found in vSLAM (Visual SLAM) technology.

This map doesn't look so intuitive compared to raster maps. It is generally generated by the vSLAM algorithm such as GPS, UWB and camera with sparse mode. The advantage is that the relative data storage amount and computation amount are relatively small, which is more common in the earliest SLAM algorithm.

3. Direct characterization

In the direct characterization method, the intermediate link of feature or grid representation is omitted, and the data read by the sensor is directly used to construct the pose space of the robot.

The picture above is an image map that directly records the ceiling image of the house. This method, like a satellite map, directly integrates sensor raw data into a map by simple processing, which is relatively more intuitive.

4, topology map

Topological map, a relatively more abstract form of map, represents the indoor environment as a topological map with nodes and associated links, where nodes represent important locations in the environment (corners, doors, elevators, stairs, etc.) ), the side indicates the connection relationship between the nodes, such as the corridor. This method only records the topological link relationship of the environment. Such maps are generally extracted from the previous types of maps through correlation algorithms.

For example, when the sweeping robot is going to clean the room, it will create such a topology map:

5. Summary

In robotics, SLAM's map construction usually refers to the creation of maps that are consistent with the geometry of the environment.

The topology map established in the general algorithm only reflects the connection relationship of each point in the environment, and can not construct a geometrically consistent map. Therefore, these topological algorithms cannot be used for SLAM.

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