activity
20192022
most citedAn Empirical Study of Low Precision Quantization for TinyML

12 citations · 12 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG202212 cited

An Empirical Study of Low Precision Quantization for TinyML

Shaojie Zhuo, Hongyu Chen, Ramchalam Kinattinkara Ramakrishnan +5

Tiny machine learning (tinyML) has emerged during the past few years aiming to deploy machine learning models to embedded AI processors with highly constrained memory and computati…

cs.RO2020

Advanced Mapping Robot and High-Resolution Dataset

Hongyu Chen, Zhijie Yang, Xiting Zhao +8

This paper presents a fully hardware synchronized mapping robot with support for a hardware synchronized external tracking system, for super-precise timing and localization. Nine h…

cs.RO2019

Improving CNN-based Planar Object Detection with Geometric Prior Knowledge

Jianxiong Cai, Jiawei Hou, Yiren Lu +3

In this paper, we focus on the question: how might mobile robots take advantage of affordable RGB-D sensors for object detection? Although current CNN-based object detectors have a…

cs.RO2019

Heterogeneous Multi-sensor Calibration based on Graph Optimization

Hongyu Chen, Sören Schwertfeger

Many robotics and mapping systems contain multiple sensors to perceive the environment. Extrinsic parameter calibration, the identification of the position and rotation transform b…

cs.RO2019

Towards Generation and Evaluation of Comprehensive Mapping Robot Datasets

Hongyu Chen, Xiting Zhao, Jianwen Luo +8

This paper presents a fully hardware synchronized mapping robot with support for a hardware synchronized external tracking system, for super-precise timing and localization. We als…