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a LiDAR-based Framework for Perception-aware Planning with Perturbation-induced Metric

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# LF-3PM    :construction: **_To be released_**
a LiDAR-based Framework for Perception-aware Planning with Perturbation-induced Metric

[🎉] Our work is accepted by IROS2024. We are grateful for the constructive feedback from the reviewers. We are currently revising the final version of the paper according to the suggestions and refactoring the source code to make it clearer and more readable. The author is struggling to apply for a PhD program and hardly has extra time to sort an easy-to-use code. 

<div align="center">
  <img src="md_floder/preview.png" width = 80% height = 80% />
</div>

## 1、Installation
ubuntu 20.04 and Noetic are recommended!
```
git clone https://github.com/cckaixin/perception_aware.git
```
### install gazebo plugins
```
sudo apt-get install ros-noetic-robot-state-publisher*
sudo apt-get install ros-noetic-joint-state-controller*
sudo apt-get install ros-noetic-controller*
sudo apt-get install ros-noetic-velocity-controllers*
sudo apt-get install ros-noetic-effort-controllers
sudo apt-get install ros-noetic-position-controllers
sudo apt-get install ros-noetic-gazebo-ros-control
sudo apt install ros-noetic-hector-gazebo
sudo apt-get install ros-noetic-effort-controllers
sudo apt-get install ros-noetic-joint-state-controller
sudo apt-get install ros-noetic-position-controllers
```
### install casadi ( C++ ) 
```
sudo apt-get install coinor-libipopt-dev
git clone https://github.com/casadi/casadi.git
cd casadi
mkdir build
cmake -DWITH_IPOPT=true ..
make -j4
sudo make install
```
## 2、Run

### launch gazebo simulation environment
```
roslaunch carsim spawn_car.launch
roslaunch carsim omni_car.launch
roslaunch carsim meca_car.launch
```

### key board control: wasd + j k
```
rosrun carsim keyboard_control_meca.py
```

### launch slam module
```
roslaunch fast_lio mapping_velodyne.launch 
roslaunch fast_lio mapping_avia.launch rviz:=true
```

### launch perception node
```
roslaunch perception_evaluator test.launch
```

### run plan manager
```
roslaunch manager test.launch
```

### trajectory tracker
```
rosrun mpc_controller mpc_controller_node
```

### evaluate slam error
```
rosrun perception_evaluator evaluate_location_error.py
```

### reset model location
```
rosservice call /gazebo/set_model_state "model_state:
  model_name: 'meca_car'
  pose:
    position:
      x: 0.0
      y: 0.0
      z: 0.0
    orientation:
      x: 0.0
      y: 0.0
      z: 0
      w: 1
  twist:
    linear:
      x: 0.0
      y: 0.0
      z: 0.0
    angular:
      x: 0.0
      y: 0.0
      z: 0.0
  reference_frame: 'world'"
```

## 3、Gpu adaptation
### **_CUDA NEEDED_ :**
Look up this [**website**](https://en.wikipedia.org/wiki/CUDA) for your 'GPU_version', and add it in **[src/PA_planner/perception_evaluator/CMakeLists.txt]**
```
SET(GPU_version 61) # 61 is for GTX 1060
```
### **_DEBUG with Nsight Systems_ :**
<div align="center">
  <img src="md_floder/nsight-sys.png" width = 80% height = 80% />
</div>

Succinct commands are put as follows, if need more guidence, go to this [**repo**](https://github.com/Pupillen/Robotics_Coding/blob/master/Profiling.md)

**Dependence:**
```
git clone https://github.com/gperftools/gperftools
cd gperftools
./autogen.sh
./configure
make
sudo make install
```
**STEP0:** Configuring Compilation Options (CUDA & profiling)

CMakeLists.txt :
```
project(perception_evaluator CXX CUDA)

SET(CUDA_TOOLKIT_ROOT_DIR "/usr/local/cuda")
SET(ENABLE_CUDA true)
SET(CMAKE_CXX_STANDARD 14)
SET(CMAKE_EXPORT_COMPILE_COMMANDS ON)
SET(GPU_version 61)  # https://en.wikipedia.org/wiki/CUDA
SET(CUDA_SEPARABLE_COMPILATION ON)
SET(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-O3 -use_fast_math;)
SET(CUDA_NVCC_FLAGS -gencode arch=compute_${GPU_version},code=sm_${GPU_version};)
SET(CUDA_USE_STATIC_CUDA_RUNTIME OFF)

find_package(CUDA REQUIRED)
find_library(LIBNVTOOLSEXT nvToolsExt PATHS ${CMAKE_CUDA_IMPLICIT_LINK_DIRECTORIES})

include_directories(
  ${CUDA_INCLUDE_DIRS}
)

add_library(LDF_accelerator src/LDF_accelerator.cu)

target_link_libraries(LDF_accelerator 
  ${CUDA_LIBRARIES}
  profiler
  ${LIBNVTOOLSEXT}
)
```
xxx.cu :
```
// Set Flags as our own nsys detector
nvtxRangePush("gpuInit");
gpuInit();
nvtxRangePop();
```
```
// Stop compiler optimization for accurate log (optional)
volatile bool tmp = false;
if (tmp) ProfilerStop();

// Add these two sentences of code to each function that needs to be analyzed
// and place them at the end of the logical code for the function. In this way, 
// when a function that needs to be analyzed is completed, these two lines of 
// code will be executed, and the ProfilerStop() function will be called to stop
// the performance analyzer, ensuring the accuracy of the analysis results
```

**STEP1:** Compile and generate executable files for subsequent analysis
```
catkin_make
```
**STEP2:** use nsys-tool to generate the profile for nsight-sys visualization
```
nsys profile -t nvtx,cuda --stats=true -f true -o gpu_profile devel/lib/perception_evaluator/perception_node_gpu nsight-sys
```
**STEP3:** use nsight-sys to visualize profiles of your target code
```
nsight-sys
```
check your CPU and GPU status by:
```
htop
nvidia-smi -l 1
```

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