Analysis of Localization Algorithms for ROS-Based Mobile Industrial Robots
Bibliographic record
Abstract
With the advancement of intelligent manufacturing and flexible automation, mobile industrial robots are increasingly being deployed in scenarios such as material handling, inspection, and collaborative operations. As one of the core technologies for mobile robots, the localization system has a direct impact on the stability and accuracy of path planning and task execution. This paper, built on the Robot Operating System (ROS) platform, systematically reviews and analyzes the implementation mechanisms and applicable scenarios of mainstream localization algorithms, with a focus on the performance characteristics of the Extended Kalman Filter (EKF) and Adaptive Monte Carlo Localization (AMCL) in industrial settings. By constructing an experimental platform that fuses multiple sensors—lidar, IMU, and wheel odometry—a series of tests comparing localization accuracy and robustness are conducted. We evaluate each algorithm's adaptability to complex conditions including dynamic occlusions, uniform environmental textures, and multipath interference. The results indicate that AMCL achieves higher positioning accuracy in static, structured environments, whereas EKF is better suited to dynamic applications suffering from sensor drift and data latency. Finally, we propose an optimization approach that integrates visual SLAM and deep-learning–based feature extraction, offering guidance for designing highly reliable localization systems for future industrial robots.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".