CAD_Refinement

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

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

2. 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

2. 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

2. 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

0. Setup system.yaml file
1. Launch Evaluation tool

roslaunch detection_evaluation detection_evaluation.launch

2. rviz file in /rviz/autoku_evaluation.rviz

3. 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:

• Jaeyoung Jo (wodud3743@gmail.com)
• AILAB Hanyang University (https://autolab.hanyang.ac.kr/)