Unsupervised Monocular Visual-inertial Odometry Network
Authors: Peng Wei, Guoliang Hua, Weibo Huang, Fanyang Meng, Hong Liu
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 {weapon, glhua, weibohuang, hongliu}@pku.edu.cn, mengfy@pcl.ac.cn |
| 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. |