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CAD_Refinement

Repository for paper "Enhanced Pose Detection of Nearby Vehicles Using LiDAR and Prior Shape for Autonomous Driving"

video

This repository provides code of prior model-based detection refinement moudle, dataset and evaluation tool
(The code will be uploaded after the review process!)


System architecture


Detection improvement (mIoU) results by various voxel size

Purpose

Enhancing LiADR-based 3D bounding box detection with prior car shape model

Method

  1. Downsample vehicle prior model into Surfel Model

Downsampling CAD model to Surfel model

  1. Execute 4-DoF Point-to-Surfel registration.

Refinement process

Demo

  • Stop Scenario Comparision

(Left) Tracking without refinement : Due to unstable detection, static objects become dynamic (Right) Tracking with refinement : Static due to stable detection

Run Algorithm

  1. Detection only
roslaunch box_point_generator box_point_generator.launch
roslaunch cad_registration cad_registration.launch
  1. Run Tracking (TBD)

Evaluation

Evaluation Setup & Sensors

  • LiDAR : Velodyne VLP-32C
  • GNSS : Novtel CPT-7, Novatel Pwrpak7

Evaluation setup and test track

Scenario Example


4 scenarios

  1. Stop Scenario (18.3 sec): Target vehicles static. Ego vehicle moves slowly.
  2. Slow Scenario (30.0 sec): The target vehicles and the ego vehicle move slowly in parallel.
  3. Fast Scenario short (159 sec) : Target vehicles and ego vehicles drive at high speed, overtaking each other
  4. Fast Scenario long (399 sec) : Target vehicles and ego vehicles drive at high speed, overtaking each other

Scenario Bag file

Google Drive Link

  1. Stop Scenario
    1. Ego: /Ego_Vehicle/stop_3_ego.bag
    2. Target 1: /Target_Vehicle_1/target_1_3.bag
    3. Target 2: /Target_Vehicle_2/target_2_3.bag
  2. Slow Scenario
    1. Ego: /Ego_Vehicle/slow_0_ego.bag
    2. Target 1: /Target_Vehicle_1/target_1_0.bag
    3. Target 2: /Target_Vehicle_2/target_2_0.bag
  3. Fast Scenario short
    1. Ego: /Ego_Vehicle/fast_3_short_ego.bag
    2. Target 1: /Target_Vehicle_2/target_1_3.bag
    3. Target 2: /Target_Vehicle_2/target_2_3.bag
  4. Fast Scenario long
    1. Ego: /Ego_Vehicle/fast_2_long_ego.bag
    2. Target 1: /Target_Vehicle_2/target_1_2.bag
    3. Target 2: /Target_Vehicle_2/target_2_2.bag

Data Preparation

  1. Need GNSS Topic from target vehicle (Existed in bag files)
  • /novatel/oem7/inspvax
  1. Ego detection topoic type should be "autoku_types::DetectsObjects" (Will be change to a general topic type.)
  • topic_name/ego_detection_topic_name

Run Evaluation Tool

  1. Setup system.yaml file
  2. Launch Evaluation tool
roslaunch detection_evaluation detection_evaluation.launch

  1. rviz file in /rviz/autoku_evaluation.rviz

  2. Type scene number when "Enter index of scene to visualize ('q' to quit)" pop up

Contact

If you have any questions, please let me know: