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Perceiving the position and orientation of objects (i.e., pose estimation) is a crucial prerequisite for robots acting within their natural environment. We present a hardware acceleration approach to enable real-time and energy efficient articulated pose estimation for robots operating in unstructured environments. Our hardware accelerator implements Nonparametric Belief Propagation (NBP) to infer the belief distribution of articulated object poses. Our approach is on average, 26× more energy efficient than a high-end GPU and 11× faster than an embedded low-power GPU implementation. Moreover, we present a Monte-Carlo Perception Library generated from high-level synthesis to enable reconfigurable hardware designs on FPGA fabrics that are better tuned to user-specified scene, resource, and performance constraints. 

Abstract The photophysical process of localized surface plasmon resonance (LSPR) is, for the first time, exploited for broadband photon harvesting in photo‐regulated controlled/living radical polymerization. Efficient macromolecular synthesis was achieved under illumination with light wavelengths extending from the visible to the near‐infrared regions. Plasmonic Ag nanostructures were in situ generated on Ag₃PO₄photocatalysts in a reversible addition‐fragmentation chain transfer (RAFT) system, thereby promoting polymerization of various monomers following a LSPR‐mediated electron transfer mechanism. Owing to the LSPR‐enhanced broadband photon harvesting, high monomer conversion (>99 %) was achieved under natural sunlight within 0.8 h. The deep penetration of NIR light enabled successful polymerization with reaction vessels screened by opaque barriers. Moreover, by trapping active oxygen species generated in the photocatalytic process, polymerization could be implemented without pre‐deoxygenation.