What does spatial intelligence connect?
Positioning answers “where am I?” Navigation adds “how do I get there?” Spatial intelligence connects position, time, environmental observations and mission context. A robot needs usable pose estimates and an understanding of surrounding space that planning and control can act on.
Four complementary layers
| Layer | Key concepts | System role |
|---|---|---|
| Satellite | GNSS, BeiDou, GPS, LEO constellations | Absolute position and time references |
| Augmentation | RTK, PPP, PPP-RTK, SBAS | Corrections that improve positioning quality |
| Fusion | GNSS/INS, VIO, SLAM, multimodal fusion | Complementary observations for continuous pose estimation |
| Intelligence | Semantic perception, VLN, mission planning | Use spatial information to understand and execute tasks |
From observations to execution
Sensing → synchronization and calibration → pose and environment estimation → path and mission planning → vehicle execution → feedback. Navigation, task payloads and machine control have distinct interfaces and responsibilities.
How this relates to HOPO
HopoEngine fuses multiple data sources. Pobot Navi integrates positioning and perception for robot platforms. LEO augmentation, low-altitude and satellite-to-ground projects require evaluation of the terminal, service coverage and delivered configuration. A technology roadmap and a function delivered on a particular model are different considerations.
Suggested learning order
- Start with GNSS, carrier phase and positioning errors.
- Compare RTK, PPP and PPP-RTK.
- Explore inertial navigation, VIO, SLAM and sensor fusion.
- Evaluate accuracy, continuity, integrity and mission outcomes in the actual environment.