Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Unsupervised Monocular Visual-inertial Odometry Network
Authors: Peng Wei, Guoliang Hua, Weibo Huang, Fanyang Meng, Hong Liu
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have been conducted on KITTI and Malaga datasets to demonstrate the superiority of Un VIO over other state-of-the-art VO / VIO methods. |
| Researcher Affiliation | Academia | 1Key Laboratory of Machine Perception, Peking University, Shenzhen Graduate School, China 2Peng Cheng Laboratory, Shenzhen, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methods in text and with diagrams (e.g., Figure 1), but it does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes are open-source1. 1https://github.com/Ironbrotherstyle/Un VIO |
| Open Datasets | Yes | KITTI Dataset. KITTI dataset [Geiger et al., 2012] serves as a prevalent driving dataset... Malaga Dataset. Malaga [Blanco-Claraco et al., 2014] is an outdoor dataset. |
| Dataset Splits | No | The paper specifies 'Seqs 00-08 excluding 03 are adopted for training and 09-10 are utilized for testing' for KITTI, and similar splits for Malaga. It does not explicitly mention a distinct validation set for model tuning. |
| Hardware Specification | Yes | All the models are implemented by using the Pytorch framework on a computer equipped with an Nvidia Ge Force GTX1080 Ti GPU. |
| Software Dependencies | No | The paper states 'All the models are implemented by using the Pytorch framework' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Adam optimizer with learning rate 10 4, β1 = 0.9, β2 = 0.999 is utilized. Images for training on both datasets are resized to 832 256, meanwhile, the IMU samples n is set to 11. The training process converges after about 100000 iterations with a batch size of 4. Besides, the length of training sequence s and window size w are 5 and 3 respectively in our experiment. The weights for loss functions are empirically given as: α1 = 1, α2 = 0.1, α3 = 0.1, α4 = 0.1, λ1 = 0.15, λ2 = 0.85. |