Simultaneous Scene Reconstruction and Auto-Calibration Using Constrained Iterative Closest Point for 3D Depth Sensor Array
Bibliographic record
Abstract
Being able to monitor a large area is essential for intelligent warehouse automation. Complete depth map of a plant floor allows Automated Guided Vehicles (AGV) to navigate the environment and safely interact with nearby people and equipment, eliminating the need for installation of guide tracks and range sensors on individual robots. A single camera does not have sufficient field of view irresolution to monitor a large scene, and a camera mounted on a moving platform introduces delays and blind spots that could put people at risk in busy areas. Multi-camera arrays are needed in order to reconstruct the scene from simultaneous captures. Existing iterative closest point (ICP) based algorithms fail to produce meaningful results due to ICP attempting to minimize Euclidean distance between non-matching pairs. This paper describes a method for accurate and computationally efficient simultaneous scene reconstruction and auto-calibration using depth maps captured with multiple downward looking overhead cameras. The proposed method extends upon standard ICP algorithm by incorporating constraints imposed by the camera setup. The common field of view constraint imposed on the ICP algorithm matches a subset of points that are simultaneously in two camera's field of view. The planar constraint restricts the search space for closest points between 2point clouds to be on a projected planar surface. To simulate a typical warehouse environment, depth maps captured from two overhead Microsoft Kinect cameras were used to evaluate the effectiveness of the proposed algorithm. The results indicate the proposed algorithm successfully reconstructed the scene and produced auto-calibrated extrinsic camera matrix, where as standard ICP algorithm did not generate meaningful results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".