For navigation and positioning, a large number of positioning technologies have emerged.
Satellite-based positioning technology
GNSS and its high-precision variants
Based on the Global Navigation Satellite System (GNSS), there are various high-precision positioning technologies such as RTK (Real-Time Kinematic), network RTK, precise point positioning (PPP), and PPP-RTK 1.
The advantages and limitations of GNSS
The positioning technology based on GNSS signals can achieve centimeter-level positioning accuracy in outdoor observation environments with good conditions. Since GNSS is a space positioning technology that uses satellite as the base station to transmit radio signals, it is prone to signal loss in indoor or urban canyon-like obstructed environments, resulting in a decline in positioning performance.
Indoor and Obstructed Environment Positioning Technology
Various wireless positioning methods
For indoor and other obstructed environments, various positioning methods have been tried, such as 5G, WiFi, Bluetooth, ultrasonic, ultra-wide band (UWB), artificial magnetic field, etc.
Challenges of Radio Positioning Technology
However, currently, radio-based positioning technologies usually require pre-deployment of base stations and have characteristics such as high ranging accuracy, strong spatial dependence, being susceptible to environmental influences, and poor environmental adaptability, so they are mostly limited to positioning applications in specific scenarios 2.
Autonomous sensor positioning technology
Inertial Measurement Unit (IMU)
The Inertial Measurement Unit (IMU) can directly obtain the motion information of the vehicle, but it is affected by zero bias noise, and long-term state estimation will lead to excessive cumulative errors 3.
Visual and LiDAR Positioning
The visual positioning technology estimates the pose through matching of corresponding points in sequence images, while the LiDAR achieves pose estimation through matching of scan point clouds.
The advantages and disadvantages of different positioning technologies.
| Positioning Technology | Positioning Accuracy | Application Scenario | Advantages | Disadvantages |
|---|---|---|---|---|
| GNSS | Sub-meter level | Outdoor | Mature technology | Degraded accuracy in obstructed environments |
| WiFi | Sub-meter level | Indoor | Low cost | Unstable positioning accuracy |
| Bluetooth | Sub-meter level | Indoor | Low power consumption | Short range, poor stability |
| Ultrasonic | Centimeter level | Indoor | High accuracy, strong anti-interference capability | Requires line of sight |
| UWB | Centimeter level | Indoor | High positioning accuracy | Susceptible to multipath effects |
| Artificial Magnetic Field | Centimeter level | Indoor | High accuracy, strong penetration capability | Limited coverage area |
| Vision | Centimeter level | Indoor/Outdoor | Low cost, autonomous positioning | Susceptible to lighting and texture variations |
| LiDAR | Centimeter level | Indoor/Outdoor | Autonomous positioning, high accuracy | Susceptible to environmental structural features |
| IMU | Diverges over time/distance | Indoor/Outdoor | Passive autonomous positioning | Long-term positioning divergence |
Simultaneous Localization and Mapping (SLAM) Technology
Overview of SLAM Technology
Definitions and Core Concepts
Real-time localization and mapping (Simultaneous Localization and Mapping, SLAM) refers to the process where a mobile vehicle can complete positioning and construct an environmental map solely through its own sensors, without the need for prior deployment of base stations. It can operate in both indoor and outdoor environments 4.
The Importance and Applications of SLAM
As one of the important research contents in navigation and location services, SLAM is not only a fundamental core module for autonomous driving, unmanned aircraft, and mobile robots, but also one of the key technologies for the integration of navigation and remote sensing in the current ubiquitous surveying and mapping field 5 6.
Main Categories of SLAM Technologies
Visual SLAM
Depending on the type of sensor, SLAM can be classified into visual SLAM and LiDAR SLAM 7. Visual SLAM refers to the process of positioning and mapping through a sequence of images captured by visual sensors. This method can fully perceive environmental information, but it heavily relies on environmental textures and lighting information. It is prone to failure in environments with weak textures or significant changes in lighting, and it is not easy to achieve dense mapping in large-scale scenes 8.
LiDAR SLAM
LiDAR SLAM realizes positioning and mapping based on continuously measured point cloud information, with high positioning accuracy. It is widely used in indoor and outdoor positioning and mapping fields.
The development trend of SLAM
Research Progress and Current Situation
With the development of SLAM, the scalability, convergence and consistency of the SLAM problem have been further studied 9. Currently, it is experiencing an era of high accuracy, high robustness, and greater intelligence 10. In recent years, there have been many reviews on SLAM, most of which are related to visual SLAM and LiDAR SLAM.
The necessity of multi-sensor fusion
With the complexity of indoor and outdoor scenes, SLAM technologies based on vision or LiDAR cannot meet the robust positioning and mapping requirements of complex indoor and outdoor scenarios. SLAM technologies based on multi-sensor fusion of vision/LiDAR/IMU can fully utilize the performance of different sensors and have a stronger adaptability in complex indoor and outdoor scenarios. They are currently at the research hotspot.
References
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- Liu Z, Li Z, Liu A, et al. LVI-Fusion: A Robust Lidar-Visual-Inertial SLAM Scheme[J]. Remote Sensing, 2024, 16(9): 1524.
- 张继贤, 刘飞. 视觉 SLAM 环境感知技术现状与智能化测绘应用展望[J]. 测绘学报, 2023, 52(10): 1617-1630.
- Khairuddin A R, Talib M S, Haron H. Review on simultaneous localization and mapping (SLAM)[C]. 2015 IEEE international conference on control system, computing and engineering (ICCSCE), 2015: 85-90.
- Cadena C, Carlone L, Carrillo H, et al. Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age[J]. IEEE Transactions on Robotics, 2016, 32(6): 1309-1332.