arXiv:2606.28607v1 Announce Type: cross Abstract: This work tackles the challenge of enhancing low-resolution LiDAR sensors for SLAM applications through a novel Deep Unrolling-based Super-Resolution (SR) model. We integrate an outlier removal module to ensure structural integrity while maintaining real-time performance. By leveraging a model-based optimization approach, our method efficiently reconstructs high-resolution point clouds while minimizing computational overhead. The proposed SR model is evaluated within a LiDAR SLAM framework, demonstrating significant improvements in pose estimat
The continuous drive for more accurate and efficient autonomous systems, particularly in robotics and vehicular navigation, necessitates breakthroughs in real-time sensor data processing.
This development significantly enhances the performance of LiDAR sensors in simultaneous localization and mapping (SLAM), crucial for robust autonomous navigation and robotics in complex environments.
LiDAR systems can now achieve high-resolution mapping with lower-cost sensors, improving both accuracy and real-time processing capabilities for autonomous platforms.
- · Autonomous vehicle manufacturers
- · Robotics companies
- · LiDAR sensor manufacturers
- · Logistics and industrial automation
More precise and reliable autonomous navigation becomes possible across various applications.
Reduced hardware costs for high-performance LiDAR solutions could accelerate the adoption of robotics and autonomous systems.
Improved spatial awareness for AI agents enhances their ability to interact with and understand physical environments, potentially enabling more complex tasks.
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Read at arXiv cs.AI