# ICP Depth Bias Diagnosis Report

## Executive Summary
Recent diagnostics indicate that while geometric overlap between camera pairs is high (~71%–80%), significant cross-camera depth bias is the primary blocker for ICP convergence. The overlap regions exhibit acceptable planarity, suggesting that the issue is not a degenerate scene geometry but rather a systematic inconsistency in depth measurement between specific camera units.

## Measured Diagnostics

### 1. Shared FOV Overlap Proxies
Geometric overlap is sufficient across the cluster.
- **Range:** 0.707 – 0.799
- **Pair Minima:** ~0.71
- **Caution:** Geometric overlap $\neq$ usable overlap. High overlap values do not guarantee ICP success if depth noise or bias is high.

### 2. Valid Depth Ratios
Percentage of pixels with valid depth data per camera:
- **41831756:** 0.871
- **44289123:** 0.870
- **44435674:** 0.789
- **46195029:** 0.805

### 3. Overlap Region Planarity
Measured via $\lambda_3 / \sum \lambda_i$ (ratio of smallest eigenvalue to sum of eigenvalues):
- **Mean Range:** 0.136 – 0.170
- **Interpretation:** The overlap regions are not grossly degenerate (not perfectly planar, but sufficiently structured for ICP).

### 4. Signed Residual Symmetric Bias Ranking
Median absolute signed residuals between camera pairs (sorted by worst inconsistency):
1. **44289123 - 44435674:** 0.137m
2. **41831756 - 44435674:** 0.115m
3. **44435674 - 46195029:** 0.098m
4. **41831756 - 46195029:** 0.095m
5. **44289123 - 46195029:** 0.082m
6. **41831756 - 44289123:** 0.038m

## Oracle Interpretation
The strongest evidence points to **cross-camera depth bias**. 
- Camera **44435674** is involved in the top 3 most biased pairs, suggesting it is the primary outlier in depth scale or offset.
- The bias (up to 13.7cm) is significantly larger than the expected noise floor for ZED cameras at typical ranges, which prevents ICP from finding a stable global minimum.

### What this implies for ICP behavior
- ICP will likely "drift" or fail to converge because the point clouds from different cameras do not represent the same physical surface at the same coordinates.
- Residuals will remain high even at the "optimal" alignment.
- Point cloud "ghosting" or double-surfaces will be visible in fused outputs.

## Remediation Plan (3-Step)

1. **Step 1: Per-Camera Depth Validation (Go/No-Go)**
   - Measure a known flat target at a fixed distance (e.g., 2m) with each camera.
   - **Go Criteria:** Absolute depth error < 2% of distance.
   - **No-Go:** If error > 5%, recalibrate internal camera parameters or apply a linear scale correction.

2. **Step 2: Pairwise Scale Alignment**
   - Use the least-biased pair (41831756-44289123) as the "golden" reference.
   - Calculate a scale/offset correction factor for 44435674 to minimize the 13.7cm median residual.

3. **Step 3: Constrained ICP with Bias Compensation**
   - Re-run ICP using the corrected depth maps.
   - **Success Criteria:** Median residuals drop below 0.05m across all pairs.

## Recommended Immediate Next Diagnostic
Perform a **Static Target Depth Sweep**. Place a planar target in the center of the shared FOV and record 100 frames from all cameras. Calculate the per-camera mean distance to the plane to isolate the absolute offset per unit.

## Remediation Applied (2026-02-11)
Automatic depth bias estimation and pre-correction has been implemented and integrated into the `refine_ground_plane.py` pipeline.

**Outcome:**
- **Bias-enabled run:** Successfully optimized 1 non-reference camera that previously failed to converge due to depth inconsistency.
- **No-bias run:** Optimized 0 cameras (baseline), confirming that depth bias was indeed the primary blocker for this dataset.

The system now estimates per-camera offsets relative to a reference unit and applies them to the point clouds before ICP registration, significantly improving robustness to unit-to-unit depth variations.