Pith. sign in

REVIEW 4 cited by

nvblox: GPU-Accelerated Incremental Signed Distance Field Mapping

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 2311.00626 v2 pith:4CAGY7CG submitted 2023-11-01 cs.RO

Alexander Millane , Helen Oleynikova , Emilie Wirbel , Remo Steiner , Vikram Ramasamy , David Tingdahl , Roland Siegwart This is my paper · ORCID
classification cs.RO
keywords computationdistancefieldmappingnvbloxroboticdenseimprovement
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Dense, volumetric maps are essential to enable robot navigation and interaction with the environment. To achieve low latency, dense maps are typically computed onboard the robot, often on computationally constrained hardware. Previous works leave a gap between CPU-based systems for robotic mapping which, due to computation constraints, limit map resolution or scale, and GPU-based reconstruction systems which omit features that are critical to robotic path planning, such as computation of the Euclidean Signed Distance Field (ESDF). We introduce a library, nvblox, that aims to fill this gap, by GPU-accelerating robotic volumetric mapping. Nvblox delivers a significant performance improvement over the state of the art, achieving up to a 177x speed-up in surface reconstruction, and up to a 31x improvement in distance field computation, and is available open-source.

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. Beyond Visibility: Real-Time Surface Accessibility Fields from Sparse LiDAR

    eess.IV 2026-08 conditional novelty 6.0 of 10

    A real-time GPU accessibility field labels each LiDAR surface point as tool-reachable or blocked using a scan-centric TSDF and precomputed tool kernels, outperforming a visibility baseline on mixed-accessibility geometry.

  2. Grasp-MPC: Closed-Loop Visual Grasping via Value-Guided Model Predictive Control

    cs.RO 2025-09 conditional novelty 6.0 of 10

    A value-guided MPC policy trained on 2 million synthetic trajectories improves closed-loop 6-DoF grasping in clutter and adapts to object perturbations.

  3. cuVSLAM: CUDA accelerated visual odometry and mapping

    cs.RO 2025-06 conditional novelty 5.0 of 10

    cuVSLAM is a CUDA-accelerated visual SLAM library supporting up to 32 cameras and reporting sub-1% KITTI trajectory error, sub-5cm EuRoC error, and real-time Jetson performance.

  4. Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments

    cs.RO 2024-11 conditional novelty 4.0 of 10

    An integrated GPU-accelerated LiDAR-visual-inertial mapping system builds labeled 3D maps of large outdoor areas in real time and uses them for autonomous point-to-point navigation.

Pith tools