Localizability-Aware Fail-Safe Localization
R&D lead for a novel localizability estimation module enabling robust multi-modal localization in dynamic indoor environments.
Problem
A visually appealing map does not guarantee reliable localization everywhere within it. Scene dynamism, sensor quality, and local geometry all affect localization confidence — yet ground truth is rarely available at runtime in deployed AMRs.
Approach
I led R&D for a localizability estimation module as part of a fault-tolerant localization provider for GPS-denied natural navigation:
- Real-time assessment of localization quality from local geometric structure
- Guides judicious fusion of visual, LiDAR, and IMU odometry streams
- Combined with map quality estimation (RGB-D and LiDAR maps)
- Architecture owner for the full fail-safe localization project across warehouses, manufacturing sites, hospitals, and malls
Results
- Novel localizability module found highly effective for GPS-denied natural navigation
- Enables proactive modality switching before localization failure
- Supports continuous operation under diverse scene dynamism and surface conditions
Localizability estimation identifies regions where localization is reliable vs. degenerate, enabling adaptive sensor fusion.
Outlier-robust plane fitting and normal estimation support geometry-aware localizability analysis.
Technologies
ROS 2 · Multi-modal odometry · Map quality estimation · Point cloud geometry · Sensor fusion