2018-10-08 星期一

The high level of English is a standard for a top student.

1. If you reveal your secret to the wind, you should not blame the wind for revealing them to the trees.

2. Everyone has potential yet to be explored. The only way to discover it is to find your extremes. ????

3. A website you often visit

---What it is
---How often you visit it
---How did you find it
---Why you often visit it

I am going to describe a website that I have been using for several years now---RenRen.
 
RenRen is a social networking website. In fact, it is the Chinese version of Facebook. They even have identical user interfaces. The name ‘Renren’ translates into everybody. And everybody can create an account and their own page on it. Despite of the large amount of users RenRen has, it manages to provide a safe environment because it requires pictures of both sides of your identification card to sign in  and they will verify your ID number. So it is very hard to create dummy accounts on Renren. 
 
This site was recommended to me by our class teacher,John. John is a sophisticated guy with exceptional people skills. He recommended it for the reason that we could better stay in touch with each other using that website. He was right.
 
I use it on a daily basis. The reason why I use it often is that we could do a ton of things with it. For instance, I use it to keep in contact with friends and find people I’ve lost touch with. We could post messages and write status updates to keep our friends informed. We could also upload photos and videos and post links from other websites. I think it is an excellent platform for spreading information and a good place for us to find people with shared interests, you know, to connect with like-minded people. That is the reason I have been using this website and will continue to do so in the near future.

4. Pains make you stronger, tears make you braver, and heartbreaks make you wiser. So thank the past for a better future.

5. Make the person mature is not the time, but the experience. subway tasted, through the nature; through the world, see light the world.

6. Live beautifully, dream passionately, love completely.

7. Looking back on life, the most painful, not failure, but not what you want to experience everything. 

8. There are always some things, when we are young, can't understand. Understand, have no longer young. 

9. “Life is about waiting for the right moment to act.So, relax.You’re not late.You’re not early.You are very much on time,” 

10. Everything will be possible,if you have a try.Nothing will be possible,if you don' try.

11. Beauty is all around, if you just open your heart to see.

12. Success depends on staying open to a better idea, even if it's not yours.

Paper

1. Improved Fast Replanning for Robot Navigation in Unknown Terrain

(http://idm-lab.org/bib/abstracts/papers/tr-dstarlite.pdf)

Abstract

Mobile robots often operate in domains that are only incompletely known, for example, when they have to move from given start coordinates to given goal coordinates in unknown terrain. In this case, they need to be able to replan quickly as their knowledge of the terrain changes. Stentz’ Focussed Dynamic A* is a heuristic search method that repeatedly determines a shortest path from the current robot coordinates to the goal coordinates while the robot moves along the path. It is able to replan one to two orders of magnitudes faster than planning from scratch since it modifies previous search results locally. Consequently, it has been extensively used in mobile robotics. In this article, we introduce an alternative to Focussed Dynamic A* that implements the same navigation strategy but is algorithmically different. Focussed Dynamic A* Lite is simple, easy to understand, easy to analyze and easy to extend, yet is more efficient than Focussed Dynamic A*. We believe that our results will make D*-like replanning methods even more popular and enable robotics researchers to adapt them to additional applications.

2. Speech Synthesis for Mixed-Language Navigation Instructions  （ignore）

 (https://www.isca-speech.org/archive/Interspeech_2017/pdfs/1259.PDF)

3. Depth Camera Based Indoor Mobile Robot Localization and Navigation

（https://www.cs.cmu.edu/~mmv/papers/12icra-BiswasVeloso.pdf）

Abstract— The sheer volume of data generated by depth cameras provides a challenge to process in real time, in particular when used for indoor mobile robot localization and navigation. We introduce the Fast Sampling Plane Filtering (FSPF) algorithm to reduce the volume of the 3D point cloud by sampling points from the depth image, and classifying local grouped sets of points as belonging to planes in 3D (the “plane filtered” points) or points that do not correspond to planes within a specified error margin (the “outlier” points). We then introduce a localization algorithm based on an observation model that down-projects the plane filtered points on to 2D, and assigns correspondences for each point to lines in the 2D map. The full sampled point cloud (consisting of both plane filtered as well as outlier points) is processed for obstacle avoidance for autonomous navigation. All our algorithms process only the depth information, and do not require additional RGB data. The FSPF, localization and obstacle avoidance algorithms run in real time at full camera frame rates (30Hz) with low CPU requirements (16%). We provide experimental results demonstrating the effectiveness of our approach for indoor mobile robot localization and navigation. We further compare the accuracy and robustness in localization using depth cameras with FSPF vs. alternative approaches that simulate laser rangefinder scans from the 3D data.

Project  (http://www.ipb.uni-bonn.de/projects/)

1. Mapping on Demand    (http://www.ipb.uni-bonn.de/projects/MoD/)

Project Description

The goal of the project is the development and testing of procedures and algorithms for the fast three-dimensional identification and mensuration of inaccessible objects on the basis of a semantically specified user inquiry. The sensor platform is a lightweight autonomously flying drone. It uses the visual information from cameras for navigation, obstacle detection, exploration and object acquisition. 
Our group will develop the appropiate models, methods and algorithms and evaluates them prototypically in suburban areas, using building structures, vegetation and stationary traffic detection as examples. Key elements of the develped methods are the problem-specific generation of models of complex spatiotemporal patterns and the explicit treatment of the uncertainty of data and models. 
One can summarize the technology necessary for the solution of these tasks with the term "Mapping on Demand". "Mapping on Demand" covers all processes, algorithms and tools for the acquisition of new data and the processing and interpretation of existing data. The goal is to derive a visual model of the spatial phenomena and draw user specified space-related conclusions, which should be in time from the point of view of the user. The development of this technology is necessary for two reasons:

• • Due to the online three-dimensional reconstruction of the environment the aircraft can react autonomously to obstacles and field-of-view obstacles and bypass them. So a very high completeness of the reconstruction can be reached.
  • Since the visual information is interpreted immediately, relevant information for the user about the object is already present during the flight or at least at the end of the flight. This information can be used for subsequent decisions.

The user of such an acquisition and exploration system is relieved concerning navigation, and gets an application-specific spatiotemporal interpreted representation of the object. The goal of the project therefore can be clearly distinguished from classical procedures of simultaneous localization and mapping (SLAM), as it concerns simultaneous navigation and interpretation. There is a very wide variety of applications so that the project members concentrate first on simple search, identification and reconstruction tasks in suburban areas. However the realization takes place in a context of science, which can use each kind of provisional technical results immediately, for example geosciences and agricultural sciences.

2. Flourish  (http://flourish-project.eu/)

To feed a growing world population with the given amount of arable land, we must develop new methods of sustainable farming that increase yield while minimizing chemical inputs such as fertilizers, herbicides, and pesticides. Precision agricultural techniques seek to address this challenge by monitoring key indicators of crop health and targeting treatment only to plants or infested areas that need it. Such monitoring is currently a time consuming and expensive activity. There has been great progress on automating this activity using robots, but most existing systems have been developed to solve only specialized tasks. This lack of flexibility poses a high risk of no return on investment for farmers.

The goal of the Flourish project is to bridge the gap between the current and desired capabilities of agricultural robots by developing an adaptable robotic solution for precision farming. By combining the aerial survey capabilities of a small autonomous multi-copter Unmanned Aerial Vehicle (UAV) with a multi-purpose agricultural Unmanned Ground Vehicle (UGV), the system will be able to survey a field from the air, perform targeted intervention on the ground, and provide detailed information for decision support, all with minimal user intervention. The system can be adapted to a wide range of farm management activities and different crops by choosing different sensors, status indicators and ground treatment packages. The gathered information can be used alongside existing precision agriculture machinery, for example, by providing position maps for fertiliser application.

This development requires improvements in technological abilities for safe accurate navigation within farms, coordinated multi-robot mission planning that enables large field survey even with short UAV flight times, multispectral three-dimensional mapping with high temporal and spatial resolution, ground intervention tools and techniques, data analysis tools for crop monitoring and weed detection, and user interface design to support agricultural decision making. As these aspects are addressed in Flourish, the project will unlock new prospects for commercial agricultural robotics in the near future.