arXiv:2606.16935v1 Announce Type: cross Abstract: Rovers rely on perception to maintain spatial maps that encode both objects and sensor quality (e.g., range reliability, lighting artifacts, data density), guiding data fusion, embedding updates, and navigation under partial observability. To study these coupled perception-navigation processes, we present CrossMaps, a real-time confidence-aware open-vocabulary semantic mapping pipeline that constructs language-queryable maps from RGB-D data. Building on VLMaps-style approaches, CrossMaps integrates multi-scale CLIP embeddings with confidence-aw
Advances in Vision-Language Models (VLMs) and increasing computational power allow for more sophisticated and real-time robotic perception systems.
This development enhances the autonomy and robustness of robotic systems, especially for navigation in unstructured or unknown environments, critical for applications like space exploration or defense.
Rovers can now construct more reliable, confidence-aware, and language-queryable semantic maps, enabling more adaptive and flexible navigation without human intervention.
- · Robotics companies
- · Space exploration agencies
- · Defense contractors
- · AI research institutions
- · Platforms reliant on pre-programmed navigation
- · Manual remote operation services
Increased autonomy and capability for planetary exploration and off-world construction, reducing mission costs and increasing scientific return.
Potential for broader adoption of similar confidence-aware semantic mapping in terrestrial autonomous vehicles and heavy machinery, improving safety and efficiency.
The proliferation of highly autonomous, intelligent machines capable of complex tasks in remote or dangerous environments without continuous human oversight, leading to new economic and geopolitical frontiers.
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Read at arXiv cs.AI