Continuous Research Loop
This page turns the repo's gap audits into a repeatable research loop. It exists so perception, SLAM, mapping, sensors, and platform coverage keep moving from discovery to atomic files instead of remaining as broad backlog rows.
For repeatable source monitoring, use the Active Frontier Source Registry. It records the sites, filters, query patterns, review cadence, and automation feasibility for active frontier discovery before candidates are routed into the canonical audits or backlog.
Loop Contract
| Stage | Output | Done when |
|---|---|---|
| Discover | Web-search and repo-audit findings | Missing techniques, methods, datasets, fundamentals, and platform topics are named with primary sources. |
| Triage | P0/P1/P2 queue | Each item has a target directory, owner type, and reason it matters to AV, indoor/outdoor, mapping, or airside use. |
| Promote | Atomic research files | One method, technique, sensor model, or platform primitive gets its own source-backed file. |
| Cross-link | Overviews, audits, README, INDEX, methodology | Readers can find the new file from the static portal without knowing the path. |
| Verify | Link checks, tests, build, stale-path scan | The corpus remains navigable and deployable through VitePress. |
| Repeat | Next queue | Remaining backlog items are smaller, better prioritized, and ready for the next agent wave. |
Current Promotion Waves
The 2026-05-09 loops promoted thirteen high-value gap clusters into first-class pages. The follow-up Perception and SLAM Gap Fill and Cross-Architecture Knowledge Base Gap Fill record the next source-backed queues discovered by the latest scout and research-agent passes; the cross-architecture follow-up also promoted 30 additional atomic pages.
| Track | Promoted coverage |
|---|---|
| Gaussian and 4D perception | SplatAD, GaussianFormer, GaussianOcc, streaming Gaussian occupancy, Cam4DOcc, StreamingFlow, Sparse4D, TacoDepth, and RaCFormer. |
| SLAM and mapping methods | MOLA, KISS-SLAM, KISS-Matcher, LVI-SAM, FAST-LIVO/FAST-LIVO2, R2LIVE/R3LIVE, Splat-SLAM, S3PO-GS, Gaussian-LIC, GS-LIVM, VIGS-SLAM, dynamic 4D Gaussian SLAM, and RadarSplat-RIO. |
| Sensor and estimation fundamentals | LiDAR, camera, IMU, GNSS/RTK, radar, event/thermal, time synchronization, multi-sensor calibration observability, wheel odometry, visible-camera hardware, and IMU/GNSS/RTK hardware. |
| First-principles foundations | Gaussian noise, Mahalanobis gating, MAP/MLE, robust statistics, mixtures, Gauss-Newton, LM, dogleg, Cholesky, QR/SVD, sparse solvers, Lie groups, PnP, ICP/GICP/NDT, occupancy grids, data association, JPDA/MHT/RFS, filters, sensor likelihoods, signal processing, radar ambiguity, CFAR, timestamping, and statistical benchmarking. |
| LiDAR artifact removal and map cleaning | LIORNet, LiSnowNet, SLiDE, TripleMixer, 3D-KNN blind-spot de-snowing, 3D-OutDet, AdverseNet, DenoiseCP-Net, classical outlier filters, broad weather artifact removal, LiDAR ghost/multipath artifacts, ERASOR, Removert, dynamic map cleaning, and artifact-removal validation. |
| ML foundations for autonomy | Perceptrons, logistic/softmax cross-entropy, MLPs, backprop/autodiff, optimization dynamics, initialization/normalization/regularization, CNNs, RNN/LSTM/GRU, attention/transformers, vision transformers, SSL, sequence models, foundation training, JEPA, and world-model first principles. |
| Dynamic/static object removal | MapCleaner, ERASOR++, 4dNDF, FreeDOM, STATIC-LIO dynamic-point removal, MotionSeg3D, MambaMOS, neural scene-flow priors, moving/static separation datasets, moved-object map-change datasets, scene-flow benchmarks, 4D occupancy benchmarks, and airside dynamic-map cleaning validation. |
| ML objective and evaluation foundations | Autoencoders/VAEs, contrastive InfoNCE, masked modeling, EBMs, tokenization/discretization, positional encodings, S4/Mamba first principles, diffusion-score-flow samplers, multi-task losses, calibration/leakage, and world-model evaluation objectives. |
| Latest perception/radar/neural-field gaps | CVFusion, 4D radar-camera occupancy, POD/FMCW LiDAR predictive detection, DrivingGaussian, HUGS, SplatFlow, DistillNeRF, TrackOcc, cross-domain LiDAR scene flow, self-supervised occupancy flow, and UniScene occupancy-centric generation. |
| Latest SLAM robust/lifelong gaps | Robust PGO/GNC/riSAM, certifiable pose-graph optimization, Kimera-RPGO/PCM, distributed multi-robot PGO, LT-mapper/Khronos, RTMap/DUFOMap, GPR localization, radar teach-repeat, and MOVES. |
| KB probability/control foundations | Probabilistic graphical models and message passing, information theory for perception/ML, uncertainty quantification/calibration/conformal prediction, constrained optimization/MPC/iLQR, and MDP/POMDP/belief-space/RL foundations. |
| Dataset and validation gaps | MUSES, sensor-corruption robustness benchmarks, open-world/OOD anomaly segmentation benchmarks, FOD and airport apron detection datasets, FOD perception validation, and knowledge-base evaluation protocol. |
| Perception/SLAM reliability gap wave | LiDAR-camera occupancy fusion, dynamic occupancy/free-space, radar-LiDAR adverse-weather detection, RobuRCDet, SAMFusion, STU, synthetic multimodal FOD benchmarks, OVAD/OVODA, open-vocabulary panoptic occupancy, RCP-Bench, V2X sequential datasets, Scan Context, LiDAR BA factors, Kimera-Multi, COVINS/COVINS-G, D2SLAM, UWB/range SLAM, OKVIS2-X, MM-LINS, event/thermal/4D-radar localization, continuous-time and volumetric-map foundations, detection/tracking foundations, fleet-data contracts, and perception/SLAM/map validation protocols. |
| Cross-architecture KB gap fill | 30 pages across safety standards, runtime/data governance, platform integration, closed-loop autonomy evaluation, regulatory/deployment maps, and company deployment evidence. |
Active Next Queue
| Priority | Queue | Next atomic files to consider |
|---|---|---|
| P0 | Perception occupancy and radar | EvOcc, DepthOcc, LinkOcc, missing-view resilient occupancy, Gaussian-rendered occupancy, 4D radar road-boundary/freespace, and Drive-OccWorld/DFIT-OccWorld where separate world-model coverage is needed. |
| P0 | Sparse and end-to-end perception | SparseBEV, DETR4D, ForeSight, SparseDrive, DiffusionDrive, SAM4D, DriveBench, and Open3DTrack. |
| P0 | Removal validation and adverse-weather datasets | Airside dust, de-icing mist, steam, glycol film, wet apron multipath, retroreflector bloom, do-not-delete hazard labels, DR-REMOVER, ExelMap, and Airport-FOD3S data-engine coverage. |
| P0 | SLAM robustness and benchmarks | DR-REMOVER, ExelMap, NDT variants, SLAM Toolbox, PIN-SLAM, ROMAN, and benchmark pages for SNAIL Radar, HeRCULES, GEODE, and COSMO-Bench. |
| P0 | Sensor and calibration fundamentals | Ultrasonic proximity models, thermal IR radiometry as a standalone file, fleet calibration operations, calibration-bay fixtures, online calibration drift response, and fiducial/corner localization. |
| P0 | First-principles extensions | Optimal experiment design for calibration, covariance consistency under robust losses, robust-loss uncertainty, epipolar/homography foundations, optical/scene flow math, constrained KKT/QP/SQP, and dataset-evaluation fundamentals. |
| P1 | Collaborative and infrastructure perception | SparseCoop, CoDS, JigsawComm, QuantV2X, TruckV2X, collaborative Gaussian occupancy, indoor open-vocabulary 3D instance segmentation, and embodied robotics 3D perception benchmarks. |
| P1 | Alternative localization sensors | Wheel/LiDAR/IMU factor graphs, infrastructure-aided localization, PG-LIO, LIR-LIVO, semantic LiDAR-inertial-wheel odometry, and newer radar/Gaussian/foundation SLAM variants. |
Promotion Rules
- Prefer one file per method or technique when the user needs depth.
- Keep family synthesis in overview files; put method evidence in method files.
- Use primary sources first: papers, official project pages, official repos, standards, or vendor documentation.
- Every generic method page should state inputs, outputs, assumptions, failure modes, AV relevance, and Domain Fit across the relevant canonical domains. Domain-specific pages should state their primary ODD and include transfer notes when the idea generalizes.
- Every sensor/foundation page should connect measurement physics to perception, SLAM, mapping, validation, and operational monitoring.
- After every wave, update the relevant coverage audit, Research Index, README, and Methodology.
Source Audits
| Audit | Role |
|---|---|
| Active Frontier Source Registry | Lists source sites, native filters, query patterns, review cadence, canonical routing rules, and semi-automation boundaries for active frontier monitoring. |
| Perception Coverage Audit | Tracks perception methods, benchmarks, datasets, and robustness gaps. |
| SLAM Coverage Audit | Tracks SLAM, odometry, localization, backend, sensor-fusion, and benchmark gaps. |
| Knowledge Gap Backlog | Tracks cross-architecture gaps outside the dedicated perception and SLAM audits. |
| Cross-Architecture Knowledge Base Gap Fill | Turns the latest broad KB web sweep into source-backed promotion queues for safety, runtime, platform, foundations, autonomy evaluation, and deployment intelligence. |