Enhanced mobile robot outdoor localization using INS/GPS integration
Bibliographic record
Abstract
An unprecedented surge of developments in mobile robot outdoor navigation was witnessed after the US government removed selective availability of the global positioning system (GPS). However, in certain situations GPS becomes unreliable or unavailable due to obstructions such as buildings and trees. During GPS outages, a positioning solution with a minimum cost is preferred for small wheeled robots. A low-cost inertial measurement unit (IMU) is a good choice to provide such a solution; however, low-cost MEMS-based inertial sensors suffer from several errors that are stochastic in nature. These errors accumulate and cause a rapid deterioration in the quality of position estimate. The purpose of this paper is to describe an enhanced low-cost 3-D navigation system using a Kalman filter (KF) that integrates odometry from wheel encoders, low cost MEMS-based inertial sensors, and GPS. The proposed technique uses reduced inertial sensor system (RISS). The RISS used here includes three accelerometers and one gyroscope aligned with the vertical axis of the body frame of the robot. The benefits of eliminating the two other gyroscopes normally used are decreasing the cost further, and improving the performance by having less inertial sensors and thus less contribution of these sensors errors towards positional errors. These two eliminated gyroscopes were used to calculate pitch and roll which are now calculated using the two horizontal accelerometers. The experimental results show that, during GPS outages, this KF with velocity update derived from the forward speed from wheel encoders is a good technique for greatly reducing localization errors. Real localization data from one trajectory is presented. This data is post-processed and some simulated GPS outages are introduced to assess the effectiveness of the proposed technique.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".