LiDAR-Based Optimized Normal Distribution Transform Localization on 3-D Map for Autonomous Navigation

Abstract

Autonomous navigation has become a topic of immense interest in robotics in recent years. Light detection and ranging (LiDAR) can perceive the environment in 3-D by creating the point cloud data that can be used in constructing a 3-D or high-definition (HD) map. Localization can be performed on the 3-D map created using a LiDAR sensor in real-time by matching the current point cloud data on the prebuilt map, which is useful in the GPS-denied areas. GPS data is inaccurate in indoor or obstructed environments, and achieving centimeter-level accuracy requires a costly real-time kinematic (RTK) connection in GPS. However, LiDAR produces bulky data with hundreds of thousands of points in a frame, making it computationally expensive to process. The localization algorithm must be very fast to ensure the smooth driving of autonomous vehicles. To make the localization faster, the point cloud is downsampled and filtered before matching, and subsequently, the Newton optimization is applied using the normal distribution transform to accelerate the convergence of the point cloud data on the map, achieving localization at 6 ms per frame, which is 16 times less than the data acquisition rate of LiDAR at 10 Hz (100ms per frame). The performance of optimized localization is also evaluated on the Kitti odometry benchmark dataset. With the same localization accuracy, the localization process is made five times faster. LiDAR map-based autonomous driving on an electric vehicle is tested in the TiHAN testbed at the IIT Hyderabad campus in real-time. The complete system runs on the robot operating system (ROS). The code will be released at https://github.com/abhishekt711/Localization-Nav.