14 citations · 17 across the 5 of their papers we have counts for
6 papers
Keeping an Eye on Things: Deep Learned Features for Long-Term Visual Localization
Mona Gridseth, Timothy D. Barfoot
In this paper, we learn visual features that we use to first build a map and then localize a robot driving autonomously across a full day of lighting change, including in the dark.…
Unsupervised Learning of Lidar Features for Use in a Probabilistic Trajectory Estimator
David J. Yoon, Haowei Zhang, Mona Gridseth +2
We present unsupervised parameter learning in a Gaussian variational inference setting that combines classic trajectory estimation for mobile robots with deep learning for rich sen…
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation
Hugues Thomas, Ben Agro, Mona Gridseth +2
We present a self-supervised learning approach for the semantic segmentation of lidar frames. Our method is used to train a deep point cloud segmentation architecture without any h…
DeepMEL: Compiling Visual Multi-Experience Localization into a Deep Neural Network
Mona Gridseth, Timothy D. Barfoot
Vision-based path following allows robots to autonomously repeat manually taught paths. Stereo Visual Teach and Repeat (VT\&R) accomplishes accurate and robust long-range path foll…
Learning Matchable Image Transformations for Long-term Metric Visual Localization
Lee Clement, Mona Gridseth, Justin Tomasi +1
Long-term metric self-localization is an essential capability of autonomous mobile robots, but remains challenging for vision-based systems due to appearance changes caused by ligh…
Building a Winning Self-Driving Car in Six Months
Keenan Burnett, Andreas Schimpe, Sepehr Samavi +5
The SAE AutoDrive Challenge is a three-year competition to develop a Level 4 autonomous vehicle by 2020. The first set of challenges were held in April of 2018 in Yuma, Arizona. Ou…