Globally-consistent three-dimensional simultaneous localization and mapping with multi-sensor fusion
Bibliographic record
Abstract
This thesis addresses the problem of globally-consistent localization and mapping simultaneously for autonomous mobile robots in an unknown and unstructured 3D environment by sensor fusion. It belongs to the research area of simultaneous localization and mapping (SLAM) in a mobile robot community. The main contribution presented in this thesis is the development of a set of new algorithms for an autonomous mobile robot with full 3D SLAM ability by multi-sensor fusion. It can be classified in the following aspects: (1) A measurement system architecture designed for mobile robot localization and mapping in a large and unknown environment. Based on the general SLAM method, a simple structure is designed by using a stereo camera and a set of range sensors to solve the 3D SLAM problem in an unknown environment. (2) Registration uncertainty for robot self-localization in 3D . This is an approach to estimate registration uncertainty where the feature correspondence-based method is used during the process of robot pose estimation. (3) Algorithms for efficient map building in large area. By using set theory, a set of new algorithms for efficient mapping building is designed during the SLAM solution processing. (4) An algorithm design of a globally-consistent 3D SLAM by sensor fusion. One sensor such as stereo camera will be used for local SLAM and another sensor such as buoys or GPS will be used for global path estimation. The estimation results from both sensors will be fused in a global coordinate system to form a globally-consistent map and path for the mobile robot. (5) A mobile robot simulation system designed for full 3D SLAM application. In this 3D animation system, a robot would navigate in a virtual space and measure the features with all equipped sensors in its view field at every time. These measurements are used to evaluate algorithms for mobile robot in any environment.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".