This repository was forked from omgteam/Didi-competition-solution

Here you can find more info about the challenge.

This repository is to provide visualization, calibration, detection ROS nodes.

Follow instructions you find in this page.

• Download the 22GB dataset from here.

$ git clone https://github.com/omgteam/Didi-competition-solution.git

$ cd Didi-competition-solution

$ catkin_make

$ source devel/setup.bash

To visualize Dataset Release 2, we need to convert topic /velodyne_packets to /velodyne_points:

First install ROS velodyne drivers in https://github.com/ros-drivers/velodyne.git, then:

$ roslaunch velodyne_pointcloud 32e_points.launch

$ roslaunch didi_visualize display_rosbag_rviz.launch rosbag_file:=PATH/NAME.bag

This module is borrowed from https://github.com/jokla/didi_challenge_ros.

Write a simple script to subscribe the msgs from the sensors' topics and publish to new topics, you can reference ./src/round1/scripts/img_example.py

To test the script, this is how to replay the bags in loops:

$ rosbag play PATH/NAME.bag -l

This is how to run the single script which subscribes and publishes images topics:

$ rosrun round1 img_example.py

It will show subscribe imgs successfully

To detect cars, we use lidar and radar sensor info to generate proposals, then project into 2D image and classify target type(car, pedestrian, cyclist, background) and regress to targets. This kind of solution can handle object detection within range of 170 meters.

Proposal type: focus point (x,y,z); 3D proposal (x,y,z,w,l,h).

Projection function: 2D box with focus point at center (box's height and width is function of distance of focus point); Projection of 3D proposal (x,y,z,w,l,h).

Classifier and Regressor: CNN classification and regressor.

How to use history detection info to avoid redundant detection and boost detection accuracy. To avoid redundant detection, recognize stationary obstacles. To boost detection accuracy, we can use trajectory smooth techiniques presented in [1].

[1] Jiang, Chunhui, et al. "A trajectory-based approach for object detection from video." Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE, 2016.