Optical Navigation Methods for Planetary Exploration
Summary
Optical navigation harnesses images captured by spacecraft to determine position and orientation relative to planets, moons or small bodies without sole reliance on ground‐based tracking. Central approaches include horizon‐based methods, which fit conic sections to the observed limb of a target body; line-of-sight measurements, in which bearing angles to known celestial objects are compared with onboard ephemerides; and terrain relative navigation, which registers surface features such as craters or landmarks against pre-existing maps. Algorithms range from analytic, non-iterative solvers for spheroidal horizons to feature-matching networks and recursive filters that fuse optical data with inertial or altimetric inputs. Advances in miniaturised cameras, real-time image processing and filter design have enabled autonomous flybys, deep-space cruise experiments and pinpoint landings. This autonomy reduces ground-station dependence, lowers operational costs for small spacecraft and increases mission robustness. Practical applications span asteroid rendezvous, lunar touchdown and Mars approach, illustrating how unified optical techniques support both global navigation and local hazard avoidance in planetary exploration.
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Optical Navigation Methods for Planetary Exploration publication trend
The graph below shows the total number of articles in optical navigation methods for planetary exploration across all publications each year (not limited to Nature Index journals).
Technical terms
Horizon-based navigation: Uses the projected outline of a celestial body’s limb in an image to infer spacecraft position and attitude by fitting conic sections to the observed horizon.
Line-of-sight navigation: Exploits measured directions to known celestial bodies as seen in onboard images, matched against stored ephemerides, to estimate spacecraft state through filtering.
Terrain relative navigation: A localisation technique that matches surface features such as craters or landmarks in camera images to pre-mapped terrain models for precise position estimation.
Extended Kalman filter (EKF): A recursive state estimation algorithm that linearises nonlinear dynamics and measurement models to fuse image-derived observations with other sensor data.
References
- A Tutorial on Horizon-Based Optical Navigation and Attitude Determination With Space Imaging Systems. IEEE Access (2021).
- On line-of-sight navigation for deep-space applications: A performance analysis. Advances in Space Research (2023).
- Implicit Extended Kalman Filter for Optical Terrain Relative Navigation Using Delayed Measurements. Aerospace (2022).
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