Robust autonomy on long missions depends on detecting and correcting sensor drift so that navigation, perception, and control remain accurate over time. Drift arises from cumulative biases, temperature shifts, mechanical wear, and environmental contamination. Left uncorrected, it degrades maps, causes localization failure, and shortens mission lifetime in terrestrial, marine, or planetary settings.
Causes and detection
Researchers identify multiple detection paths. Sebastian Thrun at Stanford University, Wolfram Burgard at the University of Freiburg, and Dieter Fox at the University of Washington describe in Probabilistic Robotics how sensor fusion and probabilistic state estimation reveal inconsistencies between modalities that signal drift. Timothy D. Barfoot at the University of Toronto documents how observability analysis in State Estimation for Robotics shows which parameters can be estimated online and which require external references. Practical detection techniques include comparing redundant sensors, using landmark matches against prior maps, monitoring residuals in an Extended Kalman Filter or particle filter, and applying statistical tools such as Allan variance to inertial sensors. In environments where absolute references are sparse, detecting slow bias changes requires careful modeling of uncertainty and long-term trend analysis.
Correction strategies and consequences
Correction combines algorithmic and operational measures. Online calibration adapts sensor parameters within the estimator, while adaptive filters and periodic loop closure in SLAM correct accumulated pose error when revisiting known features. Map-relative corrections using orbited or surveyed references are standard for planetary and large-scale missions run by institutions such as the Jet Propulsion Laboratory NASA that design resilient navigation pipelines. Redundancy and cross-checking among cameras, lidars, IMUs, and GNSS reduce single-sensor failure impact. Thermal and environmental modeling can predict bias changes so that controllers compensate before performance degrades. Human intervention remains important where autonomy cannot fully resolve ambiguities, especially in culturally sensitive or contested territories where remote access is limited.
Effective drift management increases mission autonomy and scientific return but requires trade-offs: added computational load, energy use for active recalibration, and design complexity. Integrating proven techniques from the robotics literature with mission-specific environmental models and redundant hardware yields resilient systems capable of long, reliable operation.