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LF-3PM 🚧 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. However, he promises he will try his best to upload the sorted code before 2024-11-30.

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 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 :

Succinct commands are put as follows, if need more guidence, go to this repo

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