Locus Release Performance Report (2026-04-18)
This report documents the performance of Locus v0.3.1, covering end-to-end latency, mathematical kernel efficiency, and regression accuracy.
Tier 1: End-to-End Performance (Python CLI)
Validated on the ICRA 2020 dataset and synthetic targets.
Measurements taken with RAYON_NUM_THREADS=1.
Real-World (ICRA 2020 - Scenario: forward)
Measured on all 50 images of the forward/pure_tags_images subset.
| Configuration | Recall | RMSE (px) | Latency (ms) |
|---|---|---|---|
| Locus (Soft) | 96.23% | 0.3152 | 151.12 |
| Locus (Hard) | 76.87% | 0.2567 | 92.24 |
Note: Python CLI latency includes significant FFI and result-marshalling overhead. Rust core latencies for the same workload are ~32ms (Hard) and ~42ms (Soft).
Tier 2: Micro-Benchmarking (Divan)
Fine-grained mathematical kernels measured strictly single-threaded on 1080p images. Substantial improvements observed compared to the March 2026 baseline.
Mathematical Kernels (1080p)
| Kernel | Latest Median (ms) | Baseline (2026-03-19) | Speedup |
|---|---|---|---|
| Thresholding (Apply) | 1.51 ms | 4.26 ms | 2.82x |
| Segmentation | 2.57 ms | 2.95 ms | 1.15x |
| Quad Extraction | 23.79 ms | 34.35 ms | 1.44x |
Tier 3: Rust Regression Suite
ICRA 2020 (HighAccuracy Presets)
| Scenario | Preset | Recall | RMSE (px) | Latency (ms) |
|---|---|---|---|---|
| Forward | Standard (Soft) | 96.23% | 0.3152 | 41.81 |
| Forward | Grid | 91.43% | 0.4577 | 58.53 |
| Forward | HighAccuracy | 46.31% | 0.7535 | 15.43 |
Investigation: HighAccuracy Performance
HighAccuracy exhibits lower recall on ICRA 2020 compared to Hub datasets. - ICRA 2020 (Forward): 46.3% Recall - Hub (1080p): 95.6% Recall
Root Cause: Sub-Pixel Bit Density. Analysis of the ICRA 2020 dataset reveals that a significant portion of tags are extremely small relative to their bit grid: - Forward: ~18% of tags have < 1.2 pixels per bit (PPB). - Circle: Thousands of tags have < 1.0 PPB (some as low as 0.03 PPB).
The HighAccuracy preset is optimized for maximum precision on high-quality images. It uses EdLines quad extraction and disables Laplacian sharpening to avoid distorting the Point Spread Function (PSF).
- EdLines sensitivity: Empirical testing shows EdLines recall collapses on tags with < 1.5 PPB (reaching < 1% for < 1.2 PPB), whereas the legacy ContourRdp algorithm remains robust down to ~1.2 PPB.
- Sharpening impact: Disabling sharpening (as in HighAccuracy mode) further degrades recall on small tags by smearing bit boundaries. Enabling sharpening recovers EdLines recall to 100% for tags > 2.0 PPB, but it remains ineffective for the "sub-pixel" tags prevalent in ICRA 2020.
For real-world robotics tracking where high recall on distant tags is required, the standard profile (Soft Decode + ContourRdp + Sharpening) remains the recommended configuration. The high_accuracy profile should be reserved for high-resolution near-field calibration where PPB is > 5.0.
Hub Regression (Rendered Tags - 1080p Tag36h11)
| Mode | Recall | Trans P50 (mm) | Latency (ms) |
|---|---|---|---|
| Standard | 100.0% | 5.3 mm | 21.69 |
| HighAccuracy | 95.56% | 0.7 mm | 11.44 |
Technical Updates in this Version
- Snapshot Stabilization: All regression snapshots have been updated to established v0.3.1 baselines.
- Kernel Optimization: SIMD-accelerated thresholding and segmentation provide significant throughput gains.
- Metrology Handover: GN corner covariances now propagate directly to the weighted LM pose solver in HighAccuracy mode.