Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments
Authors: Ransalu Senanayake, Lionel Ott, Simon O'Callaghan, Fabio T. Ramos
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted in road intersections of an urban environment demonstrated that spatio-temporal Hilbert maps can accurately model changes in the map while outperforming other techniques on various aspects. |
| Researcher Affiliation | Collaboration | Ransalu Senanayake University of Sydney rsen4557@uni.sydney.edu.au Lionel Ott University of Sydney lionel.ott@sydney.edu.au Simon O Callaghan Data61/CSIRO, Australia simon.ocallaghan@data61.csiro.au Fabio Ramos University of Sydney fabio.ramos@sydney.edu.au |
| Pseudocode | Yes | Algorithm 1: Querying maps for t using HF-STHM algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository. A link provided (https://goo.gl/f9c TDr) is for the dataset used in the experiments, not the implementation code. |
| Open Datasets | Yes | Our main dataset1, named as dataset 1, consists of laser scans, each with 180 beams covering 1800 angle and 30 m radius, collected from a busy intersection [6]. Figure 3 [6] shows an aerial view of the area and the location of the sensor. In Section 4.4, we used an additional dataset1 (dataset 2) of a larger intersection, as this section veriļ¬es an important part of our algorithm. 1https://goo.gl/f9c TDr |
| Dataset Splits | No | The paper describes the datasets used (dataset 1, dataset 2) but does not provide specific details on how these datasets were split into training, validation, and test sets (e.g., percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | No | The paper mentions 'a simple Python based implementation' but does not specify any particular hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper states 'a simple Python based implementation' but does not list specific software dependencies with version numbers (e.g., Python version, library versions like NumPy, SciPy, or specific ML frameworks). |
| Experiment Setup | Yes | SHM and HF-STHM used 1000 bases. DGM is an extension to [1] which calculates occupancy probability based on few past time steps. In this experiment we considered 10 past time steps and 1 m grid-cell resolution for DGM. |