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Implementation and analysis of a parallel kalman filter algorithm for lidar localization based on CUDA technology.

Lesia Mochurad¹

¹Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine.

Frontiers in Robotics and AI

|February 19, 2024

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This study introduces a parallel Kalman algorithm to accelerate Lidar localization for autonomous driving. The new method achieves 3.8x faster processing without sacrificing localization accuracy, crucial for real-time applications.

Area of Science:

Robotics and Autonomous Systems
Sensor Fusion
Computational Geometry

Background:

Navigation satellite systems (e.g., GPS) are prone to failure due to environmental and technical issues, impacting autonomous driving localization accuracy.
Lidar (Light Detection and Ranging) offers an alternative localization technology, but its integration with existing systems requires optimization.
Kalman filters enhance Lidar measurement accuracy by accounting for noise and inaccuracies.

Purpose of the Study:

To develop a computationally efficient Lidar localization algorithm for autonomous driving.
To improve the speed of Lidar-based localization without compromising accuracy.
To address the limitations of satellite navigation in challenging conditions.

Main Methods:

Keywords:

CUDA technology acceleration extended kalman filter lidar real-time systems

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Proposed a parallel Kalman algorithm implemented in three-dimensional space.

Focused on parallelizing the Kalman localization algorithm itself, not map generation.

Utilized CUDA for accelerating the Kalman filter with Lidar data.

Main Results:

Achieved a 3.8x speedup in Lidar localization computation.
Maintained localization accuracy at 3% in both parallel and non-parallel implementations.
Demonstrated the effectiveness of the parallel Kalman algorithm for real-time decision-making.

Conclusions:

The parallel Kalman algorithm significantly enhances the computational speed of Lidar localization.
This approach offers a practical solution for real-time autonomous driving, especially with large Lidar datasets.
The method provides a reliable and accurate localization solution, overcoming satellite navigation vulnerabilities.