DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors
Authors: Xiaze Zhang, Ziheng Ding, Qi Jing, Yuejie Zhang, Wenchao Ding, Rui Feng
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate our proposed Deep Point Map and current state-of-the-art algorithms on benchmark datasets. |
| Researcher Affiliation | Academia | 1School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University 2Academy for Engineering and Technology, Fudan University 3Shanghai Collaborative Innovation Center of Intelligent Visual Computing |
| Pseudocode | No | The paper describes procedures but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | 1Source code: https://github.com/Zhang Xiaze/Deep Point Map |
| Open Datasets | Yes | Semantic KITTI (Behley et al. 2019, 2021) is a widely used benchmark dataset... KITTI-360 (Liao, Xie, and Geiger 2022) includes... Mul Ran (Kim et al. 2020) is a range dataset... KITTI-Carla (Deschaud 2021) is a synthetic dataset... |
| Dataset Splits | Yes | We use the first 6 sequences as training-set. ... The split of training and testing set is based on the frame amount of approx. 6:4, without any manual picking. |
| Hardware Specification | Yes | on 6 RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions Open3D library and Adam W optimizer, but specific version numbers for these software dependencies are not provided. |
| Experiment Setup | Yes | with Adam W optimizer (Reddi, Kale, and Kumar 2019), initial learning rate lr = 10 3, weight decay wd = 10 4, cosine lr scheduler, on 6 RTX 3090 GPU. For all tasks, the network is trained for 12 epochs. ... We use the average of loss in both directions, with the hyper-parameters of εpositive = 1m, εoffset = 2m, τ = 0.1, λ1, λ2, λ3 = (1.0, 0.1, 1.0). |