# Voxgraph: Globally Consistent, Volumetric Mapping using Signed Distance Function Submaps

arXiv: https://arxiv.org/abs/2004.13154 · HTML: https://mlsanity.com/paper/2004.13154/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 26 of 26)

- [Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2101.06894/) (2021)
- [iSDF: Real-Time Neural Signed Distance Fields for Robot Perception](https://mlsanity.com/paper/2204.02296/) (2022)
- [Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians](https://mlsanity.com/paper/2405.16544/) (2024)
- [LoopSplat: Loop Closure by Registering 3D Gaussian Splats](https://mlsanity.com/paper/2408.10154/) (2024)
- [Loopy-SLAM: Dense Neural SLAM with Loop Closures](https://mlsanity.com/paper/2402.09944/) (2024)
- [PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency](https://mlsanity.com/paper/2401.09101/) (2024)
- [GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM](https://mlsanity.com/paper/2403.19549/) (2024)
- [BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusion](https://mlsanity.com/paper/2204.01139/) (2022)
- [What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Models](https://mlsanity.com/paper/2512.03422/) (2025)
- [FD-SLAM: 3-D Reconstruction Using Features and Dense Matching](https://mlsanity.com/paper/2203.13861/) (2022)
- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping](https://mlsanity.com/paper/2110.09415/) (2021)
- [SiLVR: Scalable Lidar-Visual Reconstruction with Neural Radiance Fields for Robotic Inspection](https://mlsanity.com/paper/2403.06877/) (2024)
- [NeuV-SLAM: Fast Neural Multiresolution Voxel Optimization for RGBD Dense SLAM](https://mlsanity.com/paper/2402.02020/) (2024)
- [PlaneSDF-based Change Detection for Long-term Dense Mapping](https://mlsanity.com/paper/2207.08323/) (2022)
- [Neural Graph Map: Dense Mapping with Efficient Loop Closure Integration](https://mlsanity.com/paper/2405.03633/) (2024)
- [SiLVR: Scalable Lidar-Visual Radiance Field Reconstruction with Uncertainty Quantification](https://mlsanity.com/paper/2502.02657/) (2025)
- [Deblur Gaussian Splatting SLAM](https://mlsanity.com/paper/2503.12572/) (2025)
- [EUGens: Efficient, Unified, and General Dense Layers](https://mlsanity.com/paper/2601.22563/) (2026)
- [GIRA: Gaussian Mixture Models for Inference and Robot Autonomy](https://mlsanity.com/paper/2307.00071/) (2023)
- [Neural Semantic Map-Learning for Autonomous Vehicles](https://mlsanity.com/paper/2410.07780/) (2024)
- [Memory Over Maps: 3D Object Localization Without Reconstruction](https://mlsanity.com/paper/2603.20530/) (2026)
- [HVOFusion: Incremental Mesh Reconstruction Using Hybrid Voxel Octree](https://mlsanity.com/paper/2404.17974/) (2024)
- [EUGens: Efficient, Unified, and General Dense Layers](https://mlsanity.com/paper/2410.09771/) (2024)
- [PRISM-Loc: a Lightweight Long-range LiDAR Localization in Urban Environments with Topological Maps](https://mlsanity.com/paper/2506.15849/) (2025)
- [Incremental Semantics-Aided Meshing from LiDAR-Inertial Odometry and RGB Direct Label Transfer](https://mlsanity.com/paper/2604.09478/) (2026)

## Abstract (arXiv)

Globally consistent dense maps are a key requirement for long-term robot navigation in complex environments. While previous works have addressed the challenges of dense mapping and global consistency, most require more computational resources than may be available on-board small robots. We propose a framework that creates globally consistent volumetric maps on a CPU and is lightweight enough to run on computationally constrained platforms. Our approach represents the environment as a collection of overlapping Signed Distance Function (SDF) submaps, and maintains global consistency by computing an optimal alignment of the submap collection. By exploiting the underlying SDF representation, we generate correspondence free constraints between submap pairs that are computationally efficient enough to optimize the global problem each time a new submap is added. We deploy the proposed system on a hexacopter Micro Aerial Vehicle (MAV) with an Intel i7-8650U CPU in two realistic scenarios: mapping a large-scale area using a 3D LiDAR, and mapping an industrial space using an RGB-D camera. In the large-scale outdoor experiments, the system optimizes a 120x80m map in less than 4s and produces absolute trajectory RMSEs of less than 1m over 400m trajectories. Our complete system, called voxgraph, is available as open source.