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CP-SLAM: Collaborative Neural Point-based SLAM System[NeurIPS'23]

Jiarui Hu1 · MaoMao1 Hujun Bao1 · Guofeng Zhang1 · Zhaopeng Cui1*
1 State Key Lab of CAD&CG, Zhejiang University
* Corresponding author.

This is the official implementation of CP-SLAM: Collaborative Neural Point-based SLAM System. CP-SLAM system demonstrates remarkable capabilities in multi-agent deployment and achieves state-of-the-art performance in tracking, map construction, and rendering.

CP-SLAM pipeline

Single GIF Collaboration GIF

Table of Contents
  1. News
  2. Dataset Download Link
  3. Installation
  4. Usage
    1. Run
    2. Evaluation
  5. Acknowledgement
  6. Citation

News

  • 2024.04.11 --- We have updated the README.md and are preparing to open-source our code!
  • 2024.04.26 --- Code for some functional modules, including loop detection, pose graph, federated center, and shared data structure (detailed comments will come soon).
  • 2024.05.08 --- Code for main parts, including optimizer, renderer, fusion center, and tracking and mapping modules.
  • Installation setup

Dataset Download Link

We provide the Download link to

  • Four single-agent trajectories. Each contains 1500 RGB-D frames.
  • Four two-agent trajectories. Each is divided into 2 portions, holding 2500 frames, with the exception of Office-0 which includes 1950 frames per part.
  • Two pre-trained NetVLAD models for the loop detection module.

Installation

  • Method 1 step-by-step set up(Recommended)

  • Method 2 Configure the environment in one line

Usage

Run

Evaluation

Acknowledgement

Citation

@misc{hu2023cpslam,
      title={CP-SLAM: Collaborative Neural Point-based SLAM System}, 
      author={Jiarui Hu and Mao Mao and Hujun Bao and Guofeng Zhang and Zhaopeng Cui},
      year={2023},
      eprint={2311.08013},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}