5 accepted papers
This paper tackles the problem of generating representations of underwater 3D terrain. Off-the-shelf generative models, trained on Internet-scale data but not on specialized underwater images, exhibit downgraded realism, as images of the seafloor are relatively uncommon. To this end, we introduce Dr…
Outdoor intelligent autonomous robotic operation relies on a sufficiently expressive map of the environment. Classical geometric mapping methods retain essential structural environment information, but lack a semantic understanding and organization to allow high-level robotic reasoning. 3D scene gra…
Reliable localization in robotics requires robust handling of sensor outliers, particularly in environments where acoustic or bearing measurements are noisy. We propose a replicator-dynamics-based approach for weighted group- k consistent set maximization (rGkCM) to identify the densest subsets of m…
We address the problem of image registration to a compressed 3D map. While this is most often performed by comparing LiDAR scans to the point cloud based map, it depends on an expensive LiDAR sensor at run time and the large point cloud based map creates overhead in data storage and transmission. Re…
We aim to assess the performance of LiDAR-to-map registration on compressive maps. Modern autonomous vehicles utilize pre-built HD (High-Definition) maps to perform sensor-to-map registration, which recovers pose estimation failures and reduces drift in a large-scale environment. However, sensor-to-…