Ujin Kwon · 권우진

Applied algorithms, vehicle software, telemetry runtimes, and analysis tools

Hyundai Ioniq · ROS 2 Humble · Autoware Universe · FAST-LIO · HuVILab

I implemented and validated a mapless autonomous parking system on a real Hyundai Ioniq platform. The stack uses FAST-LIO odometry, native C++ LiDAR occupancy accumulation, RViz goal-pose input, delay-aware trajectory following, and Autoware-compatible control commands wired into the Ioniq CAN control path.

Earlier Isaac Sim prototype:

Problem

  • No prebuilt parking map available.
  • The parking system had to run on top of an existing research-lab vehicle software platform without breaking the validated vehicle interface.
  • Parking had to work on the real vehicle under low-speed, reverse-capable, compute-bounded conditions with practical ROS 2 timing and visualization needs.

Architecture

FAST-LIO LocalizationLiDAR-inertial odometry aligned to the vehicle localization frame
→
C++ LiDAR Occupancy Gridtimestamp-paired pointclouds, sparse tile updates, vehicle-centered ROI
→
Goal-Pose PlannerRViz target pose to parking trajectory without prebuilt maps
→
Trajectory Followerdelay-aware pose/velocity prediction with forward/reverse gear handling
→
Ioniq Command PathAutoware-compatible commands forwarded to the vehicle CAN interface

What I Built

  • Real-vehicle parking integration that connects autonomous parking logic to the existing Ioniq research platform.
  • FAST-LIO localization bridge that aligns LiDAR-inertial odometry with the vehicle pose and TF path used by the parking system.
  • LiDAR pointcloud adapter and native C++ occupancy-grid path for map generation without prebuilt pointcloud/vector maps.
  • Occupancy accumulation with timestamp-paired raw/obstacle pointclouds, map-frame projection, log-odds sparse tiles, stale-cell decay, and ROI reduction.
  • Parking planner driven by occupancy grid, current pose, and RViz goal pose.
  • Trajectory resampler / filler / follower with curvature-aware velocity filling, gear-change stops, and low-speed forward/reverse tracking.
  • Delay-aware control using measured steering and longitudinal response delays for pose prediction, velocity prediction, and separate reference preview.
  • Runtime integration that brings up localization, mapping, planning, control output, and the vehicle command interface together.

Built vs Used

  • Used: ROS 2 Humble, Autoware components, FAST-LIO, the Hyundai Ioniq research vehicle platform, and its CAN command interface.
  • Built: localization bridge, pointcloud compatibility layer, C++ occupancy accumulation path, parking planner integration, delay-aware trajectory follower, RViz control interface, and runtime launch composition.

Validation

  • Executed low-speed mapless parking on a real Hyundai Ioniq with stable odometry and vehicle motion.
  • Ran the full path from FAST-LIO and live occupancy mapping through planning, trajectory processing, control output, and the vehicle CAN interface.
  • Measured real vehicle response delay and reflected it in follower-side pose/velocity prediction.
  • The demo above records the real-vehicle run; the Isaac Sim video is retained only as earlier development context.