fixed bugs

PREPARE_DATA.md

@@ -1,7 +1,7 @@
 ## IPBLab dataset
 Downloading IPBLab dataset from our server:
 ```shell
-cd ir-mcl && mkdir data
+cd ir-mcl && mkdir data && cd data
 wget https://www.ipb.uni-bonn.de/html/projects/kuang2023ral/ipblab.zip
 unzip ipblab.zip
 ```
@@ -35,7 +35,7 @@ There is one sequence available for the localization experiments now, the full d
 ## Intel Lab datatse, Freiburg Building 079 dataset, and MIT CSAIL dataset
 Downloading these three classical indoor 2D SLAM datasets from our server:
 ```shell
-cd ir-mcl && mkdir data
+cd ir-mcl && mkdir data && cd data
 wget https://www.ipb.uni-bonn.de/html/projects/kuang2023ral/2dslam.zip
 unzip 2dslam.zip
 ```

README.md

@@ -57,7 +57,7 @@ The code was tested with Ubuntu 20.04 with:
 
   conda install -c conda-forge pybind11
   pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116
-  pip install pytorch-lightning
+  pip install pytorch-lightning tensorboardX
   pip install matplotlib scipy open3d
   pip install evo --upgrade --no-binary evo
   ```
@@ -117,21 +117,21 @@ Due to the space limitation of the paper, we provide some experimental results a
 ### Memery cost
 We provide an ablation study on the memory cost between the occupancy grid map (OGM), Hilbert map, and our neural occupancy field (NOF). 
 
-| Maps type            | Approximate memory | Loc. method              | RMSE: location (cm) / yaw (degree)     |
-|----------------------|--------------------|--------------------------|----------------------------------------|
-| OGM (5cm grid size)  | 4.00MB             | AMCL<br>NMCL<br>SRRG-Loc | 11.11/4.15<br>19.57/3.62<br>8.74/1.68  |
-| OGM (10cm grid size) | 2.00MB             | AMCL<br>NMCL<br>SRRG-Loc | 15.01/4.18<br>36.27/4.04<br>12.15/1.53 |
-| Hilbert Map          | 0.01MB             | HMCL                     | 20.04/4.50                             |
-| NOF                  | 1.96NB             | IR-MCL                   | **6.62**/**1.11**                      |
+| Maps type             |  Approximate memory  |        Loc. method         |      RMSE: location (cm) / yaw (degree)      |
+|:----------------------|:--------------------:|:--------------------------:|:--------------------------------------------:|
+| OGM (5cm grid size)   |        4.00MB        |  AMCL<br>NMCL<br>SRRG-Loc  | 11.11 / 4.15<br>19.57 / 3.62<br>8.74 / 1.68  |
+| OGM (10cm grid size)  |        2.00MB        |  AMCL<br>NMCL<br>SRRG-Loc  | 15.01 / 4.18<br>36.27 / 4.04<br>12.15 / 1.53 |
+| Hilbert Map           |        0.01MB        |            HMCL            |                 20.04 / 4.50                 |
+| NOF                   |        1.96NB        |           IR-MCL           |             **6.62** / **1.11**              |
 
 
 ### Ablation study on fixed particle numbers
 We also provide the experiment to study the performance of global localization under the same particle numbers for all methods. We fixed the number of particles to 100,000. In the below table, all baselines and IR-MCL<sup>∗</sup> always use 100,000 particles. IR-MCL is shown for reference.
 
-| Method                                          | RMSE: location (cm) / yaw (degree)                                   |
-|-------------------------------------------------|----------------------------------------------------------------------|
-| AMCL<br>NMCL<br>HMCL<br>SRRG-Loc<br>IR-MCL<sup>∗</sup> | 11.56/4.12<br>19.57/3.62<br>20.54/4.70<br>8.74/1.68<br>6.71/**1.11** |
-| IR-MCL                                          | **6.62**/**1.11**                                                    |
+|                         Method                          |                       RMSE: location (cm) / yaw (degree)                       |
+|:-------------------------------------------------------:|:------------------------------------------------------------------------------:|
+| AMCL<br>NMCL<br>HMCL<br>SRRG-Loc<br>IR-MCL<sup>∗</sup>  | 11.56 / 4.12<br>19.57 / 3.62<br>20.54 / 4.70<br>8.74 / 1.68<br>6.71 / **1.11** |
+|                         IR-MCL                          |                              **6.62** / **1.11**                               |
 
 ## Citation
 

environment.yml

@@ -27,6 +27,7 @@ dependencies:
   - xz=5.2.10=h5eee18b_1
   - zlib=1.2.13=h5eee18b_0
   - pip:
+    - --extra-index-url https://download.pytorch.org/whl/cu116
     - addict==2.4.0
     - aiohttp==3.8.3
     - aiosignal==1.3.1
@@ -57,7 +58,7 @@ dependencies:
     - fsspec==2023.1.0
     - idna==3.4
     - importlib-metadata==6.0.0
-    - ipykernel==6.20.2
+    - ipykernel==6.21.1
     - ipython==8.9.0
     - ipywidgets==8.0.4
     - itsdangerous==2.1.2
@@ -90,6 +91,7 @@ dependencies:
     - platformdirs==2.6.2
     - plotly==5.13.0
     - prompt-toolkit==3.0.36
+    - protobuf==3.20.1
     - psutil==5.9.4
     - ptyprocess==0.7.0
     - pure-eval==0.2.2
@@ -113,6 +115,7 @@ dependencies:
     - six==1.16.0
     - stack-data==0.6.2
     - tenacity==8.1.0
+    - tensorboardx==2.5.1
     - threadpoolctl==3.1.0
     - torch==1.13.1+cu116
     - torchmetrics==0.11.1

shells/pretraining/fr079.sh

@@ -1,4 +1,3 @@
-cd ~/ir-mcl
 python train.py \
   --root_dir ./data/fr079 --N_samples 256 --perturb 1 --noise_std 0 --L_pos 10 \
   --feature_size 256 --use_skip --seed 42 --batch_size 1024 --chunk 262144 \

shells/pretraining/intel.sh

@@ -1,4 +1,3 @@
-cd ~/ir-mcl
 python train.py \
   --root_dir ./data/intel --N_samples 1024 --perturb 1 \
   --noise_std 0 --L_pos 10 --feature_size 256 --use_skip --seed 42 \

shells/pretraining/ipblab.sh

@@ -1,4 +1,3 @@
-cd ~/ir-mcl
 python train.py \
   --root_dir ./data/ipblab --N_samples 256 --perturb 1 --noise_std 0 --L_pos 10 \
   --feature_size 256 --use_skip --seed 42 --batch_size 1024 --chunk 262144 \

shells/pretraining/mit.sh

@@ -1,4 +1,3 @@
-cd ~/ir-mcl
 python train.py \
   --root_dir ./data/mit --N_samples 1024 --perturb 1 --noise_std 0 --L_pos 10 \
   --feature_size 256 --use_skip --seed 42 --batch_size 512 --chunk 262144 \