Stereo Visual SLAM for Autonomous Vehicles: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Simultaneous Localization and Mapping (SLAM) problem, where an autonomous vehicle moving in an unknown environment attempts to sense and map its surroundings while recognizing its own location and trajectory within the map, has always been a notable and popular research topic in the field of computer vision, robotics and artificial intelligence. Among the various types of solutions relying on different sensor modalities such as the global positioning system (GPS), radio signals, lidar, etc., vision-based solutions are of major interest nowadays because most cameras are low-cost and rich information gathering, especially for the stereo cameras. In this paper, different technologies of visual SLAM, where the main sensors are cameras, are surveyed with an emphasis on methodologies using stereo cameras. Some state-of-the-art open-source stereo visual SLAM frameworks are also discussed and compared. Finally, a general discussion of the challenges in terms of accuracy, processing time, cost, etc. is provided. The main purpose of this review is to provide a comprehensive overview of public available stereo visual SLAM frameworks and their corresponding pros and cons in different real-world scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it