Iterative Continuous Convolution for 3D Template Matching and Global Localization
Authors: Vitor Guizilini, Fabio Ramos
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show substantial speed gains over standard discrete convolution techniques, such as sliding window and fast Fourier transform, along with a significant decrease in memory requirements, without accuracy loss. |
| Researcher Affiliation | Academia | Vitor Guizilini, Fabio Ramos School of Information Technologies, The University of Sydney, Australia e-mail: {vitor.guizilini@;fabio.ramos}sydney.edu.au |
| Pseudocode | Yes | Algorithm 1 Pseudo-code for the continuous convolution between two Hilbert Maps |
| Open Source Code | No | The paper does not contain an unambiguous statement of code release or a link to a repository for the methodology described. |
| Open Datasets | No | Two large-scale real datasets were used, representing a structured corridor (n = 474557) and an outdoor area (n = 670584). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | All computations were performed on a i7/2.60 x 8 GHz notebook, with multi-threading wherever possible. |
| Software Dependencies | No | The paper mentions the 'PCL library' but does not provide specific version numbers for any software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | To enforce sparsity, only the Q nearest clusters from each data point are selected to produce its feature vector (in all experiments, a value of Q = 3 was used). |