Name
Abstract | ||
|---|---|---|
The starting point of any Smart City approach is knowing what is in the city and the location of city assets. We propose a general, automated approach to inventorying and monitoring outdoor city infrastructure using common sensors: namely a GPS, IMU, and camera. The presented mapping algorithm operates in the mobile sensing paradigm, using observations from a moving vehicle to construct a map of landmark location estimates whose uncertainty decreases linearly with the number of observations, robust to both translational and angular error to first order. The algorithm is adaptable to many applications given an appropriate image classifier. We apply our algorithm to automatically locate and inventory city streetlights and demonstrate its performance using both numerical simulation and field experiments. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISC2.2015.7366190 | 2015 IEEE First International Smart Cities Conference (ISC2) |
Keywords | Field | DocType |
landmark mapping,unbiased observations,smart city,city assets,outdoor city infrastructure,inventorying,GPS,IMU,camera,mapping algorithm,landmark location,image classifier,inventory city streetlights,numerical simulation | Computer vision,Noise measurement,Computer simulation,First order,Computer science,Smart city,Artificial intelligence,Inertial measurement unit,Mapping algorithm,Landmark,Assisted GPS | Conference |
Citations | PageRank | References |
2 | 0.48 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jason S. Ku | 1 | 11 | 4.26 |
| stephen ho | 2 | 2 | 0.48 |
| sanjay e sarma | 3 | 5 | 0.91 |