Pith. sign in

REVIEW 4 cited by

DiTer++: Diverse Terrain and Multi-modal Dataset for Multi-Robot SLAM in Multi-session Environments

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2412.05839 v1 pith:DYBPS457 submitted 2024-12-08 cs.RO

Juwon Kim , Hogyun Kim , Seokhwan Jeong , Youngsik Shin , Younggun Cho This is my paper · ORCID
classification cs.RO
keywords datasetenvironmentslarge-scaleconditionsditerdiversedynamicmapping
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We encounter large-scale environments where both structured and unstructured spaces coexist, such as on campuses. In this environment, lighting conditions and dynamic objects change constantly. To tackle the challenges of large-scale mapping under such conditions, we introduce DiTer++, a diverse terrain and multi-modal dataset designed for multi-robot SLAM in multi-session environments. According to our datasets' scenarios, Agent-A and Agent-B scan the area designated for efficient large-scale mapping day and night, respectively. Also, we utilize legged robots for terrain-agnostic traversing. To generate the ground-truth of each robot, we first build the survey-grade prior map. Then, we remove the dynamic objects and outliers from the prior map and extract the trajectory through scan-to-map matching. Our dataset and supplement materials are available at https://sites.google.com/view/diter-plusplus/.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation

    cs.RO 2026-02 conditional novelty 7.0 of 10

    GrandTour releases 49 multi-modal legged-robot missions (>10 km, >5 h) with LiDAR, camera, IMU, depth, proprioception, and mm-level RTK-GNSS/total-station ground truth, plus a 52-method state-estimation benchmark.

  2. CU-Multi: A Dataset for Multi-Robot Collaborative Perception

    cs.RO 2025-09 conditional novelty 7.0 of 10

    CU-Multi provides eight multi-session trajectories in two outdoor environments with RGB-D, RTK GPS, semantic LiDAR, and refined ground truth for evaluating collaborative SLAM.

  3. COSMO-Bench: A Benchmark for Collaborative SLAM Optimization

    cs.RO 2025-08 conditional novelty 7.0 of 10

    A new suite of 24 realistic collaborative SLAM optimization benchmarks derived from real LiDAR data with simulated inter-robot communication.

  4. CU-Multi: A Dataset for Multi-Robot Data Association

    cs.RO 2025-05 conditional novelty 6.0 of 10

    CU-Multi is a new public multi-robot dataset with controlled trajectory overlaps, dense semantic LiDAR labels, and geospatially aligned poses for evaluating data association in collaborative SLAM.

Pith tools