CP-SLAM: Collaborative Neural Point-based SLAM System
Authors: Jiarui Hu, Mao Mao, Hujun Bao, Guofeng Zhang, Zhaopeng Cui
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various datasets demonstrate the superiority of the proposed method in both camera tracking and mapping. ... We evaluate our proposed collaborative SLAM system in two aspects, both single-agent experiments with loop closure and two-agent experiments, of varying sizes and complexity. ... We also conduct ablation studies to show the importance of modules in the proposed system. |
| Researcher Affiliation | Academia | Jiarui Hu1, Mao Mao1, Hujun Bao1, Guofeng Zhang1, Zhaopeng Cui1 1State Key Lab of CAD&CG, Zhejiang University |
| Pseudocode | No | The paper does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for CP-SLAM is publicly available. |
| Open Datasets | Yes | For reconstruction assessment, we utilize the synthetic dataset Replica [35], equipped with a high-quality RGB-D rendering SDK. ... [35] Julian Straub, Thomas Whelan, Lingni Ma, Yufan Chen, Erik Wijmans, Simon Green, Jakob J Engel, Raul Mur-Artal, Carl Ren, Shobhit Verma, et al. The replica dataset: A digital replica of indoor spaces. ar Xiv preprint ar Xiv:1906.05797, 2019. |
| Dataset Splits | No | The paper describes the structure of the datasets used (e.g., number of frames, single-agent vs. multi-agent trajectories) but does not provide explicit train/validation/test dataset splits (e.g., percentages or sample counts) for their experimental setup. |
| Hardware Specification | Yes | CP-SLAM system runs an RGB-D sequence on an NVIDIA RTX3090 GPU. In the two-agent experiment, we need an additional RTX3090 as the central server. |
| Software Dependencies | No | The paper mentions using the "FRNN library" and the "Open3D" library, but it does not specify version numbers for these or any other software components used in the experiments. |
| Experiment Setup | Yes | In all our experiments, we set Nnear = 16, Nuni = 4, λ1 = 0.2, Dl = 0.001m, r = 0.15m, ρ = 0.14m, M1 = 3000, M3 = 3136, M2 = 1500. We extract a keyframe every 50 frames and perform map optimization and point cloud supplementation every 10 frames. For single-agent experiments, we optimize the neural field for 200 iterations. For two-agent experiments... we reduce the number of iteration steps to 150. |