A Diversified Generative Latent Variable Model for WiFi-SLAM
Authors: Hao Xiong, Dacheng Tao3841
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments illustrate that the method performs Wi Fi localization more accurately than other label-free methods. In this section, we compare our proposed method with Isomap and GPLVMbased Wi Fi-SLAM for robot localization. The mean localization error over six localization runs is reported in Fig. 4. |
| Researcher Affiliation | Academia | Hao Xiong, Dacheng Tao Centre for Artificial Intelligence, University of Technology Sydney, Australia hao.xiong@student.uts.edu.au dacheng.tao@uts.edu.au |
| Pseudocode | No | The paper provides algorithmic descriptions within the text and mathematical formulations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository links, explicit statements of code release, or mention of code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | The dataset used here is presented in (Ferris, Fox, and Lawrence 2007). |
| Dataset Splits | No | The paper mentions 'We cross-validate each DGLVM result by performing localization with the remaining test trace' but does not provide specific details on the training, validation, or test dataset splits (e.g., percentages, absolute counts, or type of k-fold cross-validation). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Here, we choose the dimensionality of latent variable q and parameter λ to be 10 and 0.01, respectively. |