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 [1].
Modality-Agnostic Topology Aware Localization
Authors: Farhad Ghazvinian Zanjani, Ilia Karmanov, Hanno Ackermann, Daniel Dijkman, Simone Merlin, Max Welling, Fatih Porikli
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results demonstrate decimeter-level accuracy for localization using different sensory inputs. |
| Researcher Affiliation | Industry | Farhad G. Zanjani Ilia Karmanov Hanno Ackermann Daniel Dijkman Simone Merlin Max Welling Fatih Porikli Qualcomm AI Research EMAIL |
| Pseudocode | Yes | Algorithm 1: Jointly learning the embedding and the transportation plan. |
| Open Source Code | No | The paper references 'https://github.com/the Jolly Sin/mazelib (GNU General Public License v3.0)' which is a third-party library used for synthetic data, but does not provide a link or explicit statement for the open-sourcing of their own method's code. |
| Open Datasets | Yes | We setup an experiment by using the i Gibson dataset [Shen et al., 2020]. To validate our approach without the added complexities of data, we ο¬rst setup an experiment with a simple 2D Maze environment1 [Henriques and Vedaldi, 2018, Parisotto and Salakhutdinov, 2017]. 1https://github.com/the Jolly Sin/mazelib (GNU General Public License v3.0) |
| Dataset Splits | No | The paper mentions 'We create several image sequences by navigating the camera through all rooms/zones of each environments. We leave out some of the image sequences for the test set.' for the iGibson dataset, but does not provide specific percentages or counts for train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., specific library versions or framework versions like PyTorch 1.9). |
| Experiment Setup | No | For details about the implementation, hyper-parameters and training please refer to the supplementary material. This indicates that specific experimental setup details are not in the main text. |