The NATURE autonomy stack: an open-source stack for off-road navigation
Bibliographic record
Abstract
Off-road autonomous navigation remains an ongoing challenge for autonomous ground vehicles (AGV). The challenges of navigating in an unstructured environment include identifying and detecting both positive and negative obstacles, distinguishing navigable from non-navigable vegetation, identifying soft soil, and negotiating rough or sloping terrain. While many recent works have dealt with various aspects of the off-road navigation problem, up to now there has not been a free and open-source autonomy stack for off-road that included integrated modules for perception, planning, and control. Therefore, we have recently developed the NATURE (Navigating All Terrains Using Robotic Exploration) autonomy stack as a publicly available resource to facilitate the advancement of off-road navigation research. The NATURE stack is implemented using the Robotic Operating System (ROS) and can be built to work with both ROS-1 and ROS-2. The modular nature of the NATURE stack makes it an ideal resource for researchers who want to evaluate a particular algorithm for perception, planning, or control without developing an entire navigation stack from scratch. NATURE features several options for both global and local path planning including A*, artificial potential field, and spline-based planning, as well as multiple options for perception including a simple geometrically based obstacle finder and more advanced custom traversability algorithm derived from 3D lidar. In this presentation we give an overview of the NATURE stack and show some past uses of the stack in both simulated and field experiments.
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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 | 0.001 | 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".