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In this project, I will attempt to make fully autonomous of our small rescue robot on various terrain. It will consist of localisation(6DoF), planning and execution(flippers & wheels).
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This is the project during my research visiting at Andread Nuechter’s group. The goal is to build efficient feature for very large point cloud matching, e.g., the city data, which is a challenge due to the large size of data and the weak capability of recent descriptor in such a dataset.
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This project propose a very inexpensive method on compute the rescue robot morphology (with flipper) in various terrain.
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In this project, randomized sketches has been applied on kernel ridge regression for stochastic learning of DNN feature extractor.
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The problem want to solve is to generate the topological map incrementally during the investigation of robot such that the motion planning can benefit from it.
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In this project, a real time GPOM has been provided.
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This project consists of area graph (ICAR 2019), passage graph and hierarchical area graph (My bachelor thesis).
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This is my second project about DNN. In this project manage to order people label a crowd counting dataset and get very familiar to tensorflow.
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This is my first research project. Though not good, but still rememberable for me. The report is in github.