PointNetPGAP: PointNetPGAP-SLC: A 3D LiDAR-based Place Recognition Approach with Segment-level Consistency Training for Mobile Robots in Horticulture
You can install PointNetGAP locally in your machine. We provide an complete installation guide for conda.
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Create conda environment with python:
conda create -n pr_env python=3.9.4 -
Activate conda environment
conda activate pr_env -
Install cuda 11.7.0
conda install -c "nvidia/label/cuda-11.7.0" cuda-toolkit -
Install Pytorch 2.0.1
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia -
Testing installation
.... -
Install sparse
pip install --upgrade git+https://github.com/mit-han-lab/torchsparse.git@v1.4.0
6.1 sudo apt-get install g++
6.2 sudo apt-get install libsparsehash-dev
- Install other dependencies
pip install -r requirements.txt
python script_train.py
python train_knn.py
--network PointNetPGAPLoss # networl_name
--train 1 # [1,0] [Train,Test] Train or test
--dataset_root path/to/dataset/root # path to Dataset
--val_set
--memory RAM # [DISK, RAM]
--device cuda # Device
--save_predictions path/to/dir # To save the predictions
--epochs 200
--max_points 10000
--experiment experiment_name
--feat_dim 16
--eval_batch_size 15
--mini_batch_size 1000
--loss_alpha 0.5
In the script_eval.py, edit:
dataset_root = path/to/dataset/root
resume_root = path/to/descriptors
Then, Run:
python script_eval.py
Download Descriptors and Predictions here
The HORTO-3DLM dataset comprises four sequences OJ22, OJ23, ON22, and SJ23; Three sequences from orchards, namely from apples and cherries; and one sequence from strawberries;