#Autonomous Vehicle Project 2020

General Information

My personal repository contains what I'm learning through this course Autonomous Vehicles Project Autumn 2020 from the University of Tartu

Software Stack

• Baidu Apollo v5.5+ (git clone from here)
• LGSVL Simulator (documentation from here)
• Docker Engine (documentation from here)

Prerequisites

• Ubuntu 16.04 or later (Ubuntu 18.04 is preferred)
• Nvidia graphics card (required for Perception)
• Nvidia proprietary driver (>=410.48) must be installed
• Docker 19.03+

Installation

Following tutorial from Apollo github page

NVIDIA GPU Driver

sudo apt-get update
sudo apt-add-repository multiverse
sudo apt-get update
sudo apt-get install nvidia-driver-440

Type nvidia=smi to check if NVIDIA GPU works fine

Docker Engine

• Apollo 6.0+ requires Docker 19.03+ to work properly
• Follow this tutorial

NVIDIA Container Toolkit

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get -y update
sudo apt-get install -y nvidia-container-toolkit

Restart Docker daemon for the changes above to take effect. sudo systemctl restart docker

Apollo Setup

• clone the repository git clone https://github.com/ApolloAuto/apollo
• launch the container and mount a few volumes ./docker/scripts/dev_start.sh
• enter the container ./docker/scripts/dev_into.sh
• build apollo ./apollo.sh build_opt_gpu (optimized, not debug, with GPU support)

NOTE The Apollo build may fail on machines with less than 1GB of RAM per CPU core due to aggressive parallelization in the build, as discussed in Apollo issue 7719.

Running Apollo via LGSVL Simulator

• enter the container from Apollo root folder ./docker/scripts/dev_into.sh
• activate dreamview ./scripts/bootstrap_lgsvl.sh
• activate the bridge ./scripts/bridge.sh
• more instructions from here

Sample Demonstration Video

Record LiDAR Point Cloud

• Make sure these 3 channels are working (green lit)
  • /apollo/sensor/lidar128/compensator/PointCloud2
  • /apollo/sensor/gnss/odometry
  • /apollo/localization/pose
• run this command cyber_recorder record -c /apollo/sensor/lidar128/compensator/PointCloud2 /apollo/sensor/gnss/odometry /apollo/localization/pose
• Press CTLR+C to stop the recording
• The recording file will available on root path /apollo, for example file name will be like this 20201122140558.record.00000

Generate Local Map

Reference

Method 1

• Before generating, make sure we already have recording file from cyber_recorder command
• Run this command: ./scripts/msf_simple_map_creator.sh /apollo/recording/ /apollo/modules/calibration/data/Lincoln2017MKZ_LGSVL/velodyne_params/velodyne128_novatel_extrinsics.yaml 10 outmap (assuming /apollo/recording/ is the path where record file located)
• Image output example

Method 2

Extract Point Cloud

• Extract Point Cloud from recording file scripts/msf_record_parser.sh [RECORDINGS_FOLDER] [OUTPUT_FOLDER]
• For example: ./scripts/msf_record_parser.sh /apollo/20201122140558.record/ /apollo/20201122140558.record/record_parser/
• Point clouds output in /apollo/20201122140558.record/record_parser/

Interpolate Poses

• Pose interpolation fixes the pose file, such that the location and timestamps of the locations match LiDAR scans.
• Command: ./scripts/msf_poses_interpolator.sh [input_poses_path] [ref_timestamps_path] [extrinsic_path] [output_poses_path]
• Example: ./scripts/msf_poses_interpolation.sh /apollo/20201122140558.record/record_parser/00000/pcd/fusion_loc.tx t /apollo/20201122140558.record/record_parser/00000/pcd/pcd_timestamp.txt /apollo/modules/calibration/data/Lincoln2017MKZ_LGSVL/velodyne_params/velodyne128_novatel_extrinsics.yaml /apollo/20201122140558.record/record_parser/00000/pcd/odometry_loc.txt
• Repeat it for /apollo/recording/record_parser/00001, /apollo/recording/record_parser/00002. etc

Run msf_local_map_creator.sh

• Command ./scripts/msf_local_map_creator.sh [pcd folder] [pose file] [zone id] [map folder]
• Example ./scripts/msf_local_map_creator.sh /apollo/20201122140558.record/record_parser/00000/pcd/ /apollo/20201122140558.record/record_parser/00000/pcd/odometry_loc.txt 10 /apollo/20201122140558.record/outmap/
• Repeate it for /apollo/recording/record_parser/00001, /apollo/recording/record_parser/00002. etc
• Output in /apollo/20201122140558.record/outmap/lossy_map

Record Waypoints and Replay it

Reference

Record

• Run this two commands ./scripts/rtk_recorder.sh setup and ./scripts/rtk_recorder.sh start
• Make sure ./scripts/bridge.sh is connected
• Run car in simulator to record waypoints
• Finish recording by pressing Ctrl + C
• Recorded waypoint are stored in the apollo/data/log/garage.csv

Replay the record

• Put the car into initial location, by press F12 in simulator
• Run ./scripts/rtk_player.sh start

Testing on the Lexus Car

• Real testing of Apollo framework on Lexus car, documented here

Reference(s)

• Credits to Tanya Shtym