TartanAir
Synthetic, photo-realistic trajectories with challenging motion and environment conditions for visual SLAM research.
About
TartanAir uses simulation to expose visual odometry and SLAM systems to motion patterns and environments that are difficult to collect systematically in the real world.
Because ground truth and sensor streams are available together, the dataset is well suited to diagnosing where estimated trajectories diverge and whether failures correlate with motion, texture, lighting, or scene structure.
Suggested agent workflows
Copy a prompt into your agent of choice. Each workflow starts small, respects the source terms, and makes the result inspectable in Rerun.
Audit a training slice
Fetch TartanAir from its official source at https://theairlab.org/tartanair-dataset/. Read and preserve its source terms and citation requirements. Build a reproducible manifest for a representative slice, then load it into Rerun. Inspect the Stereo video, Depth, Poses streams for timestamp gaps, missing samples, calibration problems, and annotation inconsistencies. Summarize the findings and save both the validation script and a Rerun recording that makes every issue easy to review.
Build a baseline pipeline
Use TartanAir to scaffold a baseline pipeline for Odometry, SLAM, Depth. Start with a small deterministic cohort and document the selection criteria. Create the dataset adapter, preprocessing steps, train and validation splits, and one lightweight baseline. Log inputs, labels, predictions, losses, and representative failures to Rerun so I can inspect the full experiment timeline. Include exact setup and reproduction commands.
Find useful failure cohorts
Analyze TartanAir and propose a practical cohort taxonomy for Odometry, SLAM, Depth. Use available metadata and the Stereo video, Depth, Poses signals to identify hard cases, rare conditions, coverage gaps, and possible leakage between splits. Materialize the cohorts as queryable metadata, log examples from each cohort to Rerun, and produce a short report explaining which cohorts should become regression tests. Do not infer labels that are not present in the source data.