Heterogeneous LiDAR Early Fusion and Learned Re-Ranking Strategy for Robust Long-Term Place Recognition in Unstructured Environments
arXiv:2606.13503v1 Announce Type: cross Abstract: Robust localization in unstructured environments, such as agricultural fields, is a critical challenge for autonomous systems. LiDAR sensors provide detailed 3D information about the environment and are invariant to lighting conditions. For this reason, LiDAR-based place recognition methods have gained significant attention. In this paper, we propose MinkUNeXt-VINE++, a novel approach that combines early fusion of heterogeneous LiDAR data from two sensors (Livox Mid-360 and Velodyne VLP-16) and a learned re-ranking strategy in inference time. T
Rapid advancements in AI and sensor technology are enabling more sophisticated and robust autonomous system capabilities, addressing long-standing challenges in unstructured environments.
Improved robust localization in complex environments is crucial for the wider deployment of autonomous systems, impacting sectors from agriculture to logistics and defense.
Autonomous systems can now navigate and operate more reliably in previously challenging unstructured environments, reducing dependence on highly structured settings.
- · Agriculture tech companies
- · Autonomous vehicle developers
- · Robotics companies
- · LiDAR sensor manufacturers
- · Companies relying on manual labor in challenging environments
Increased efficiency and safety for autonomous operations in industries like farming and mining.
Accelerated development and adoption of autonomous robots and vehicles in off-road or unpredictable settings.
Enhanced supply chain resilience and food security through automated agricultural practices capable of operating in diverse conditions.
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Read at arXiv cs.AI