From e7c7477081aa4ee50e55961ebc374957a7a68eb6 Mon Sep 17 00:00:00 2001 From: Martin Miller Date: Mon, 7 Jun 2021 12:01:06 -0400 Subject: Ready for AV Job --- publications.bib | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 publications.bib (limited to 'publications.bib') diff --git a/publications.bib b/publications.bib new file mode 100644 index 0000000..ffa875c --- /dev/null +++ b/publications.bib @@ -0,0 +1,23 @@ +@article{miller2017, +author = {Martin Miller and Soon-Jo Chung and Seth Hutchinson}, +title ={The Visual–Inertial Canoe Dataset}, +journal = {The International Journal of Robotics Research}, +volume = {37}, +number = {1}, +pages = {13-20}, +year = {2018}, +doi = {10.1177/0278364917751842}, + +URL = { + https://doi.org/10.1177/0278364917751842 + +}, +eprint = { + https://doi.org/10.1177/0278364917751842 + +} +, + abstract = { We present a dataset collected from a canoe along the Sangamon River in Illinois. The canoe was equipped with a stereo camera, an inertial measurement unit (IMU), and a global positioning system (GPS) device, which provide visual data suitable for stereo or monocular applications, inertial measurements, and position data for ground truth. We recorded a canoe trip up and down the river for 44 minutes covering a 2.7 km round trip. The dataset adds to those previously recorded in unstructured environments and is unique in that it is recorded on a river, which provides its own set of challenges and constraints that are described in this paper. The dataset is stored on the Illinois Data Bank and can be accessed at: https://doi.org/10.13012/B2IDB-9342111\_V1. } +} + + -- cgit v1.1