activity
20182022
most citedAny Way You Look At It: Semantic Crossview Localization and Mapping with LiDAR

47 citations · 48 across the 4 of their papers we have counts for

collaborators

10 papers

cs.RO202247 cited

Any Way You Look At It: Semantic Crossview Localization and Mapping with LiDAR

Ian D. Miller, Anthony Cowley, Ravi Konkimalla +5

Currently, GPS is by far the most popular global localization method. However, it is not always reliable or accurate in all environments. SLAM methods enable local state estimation…

cs.RO20221 cited

DSOL: A Fast Direct Sparse Odometry Scheme

Chao Qu, Shreyas S. Shivakumar, Ian D. Miller +1

In this paper, we describe Direct Sparse Odometry Lite (DSOL), an improved version of Direct Sparse Odometry (DSO). We propose several algorithmic and implementation enhancements w…

cs.CV2019

PST900: RGB-Thermal Calibration, Dataset and Segmentation Network

Shreyas S. Shivakumar, Neil Rodrigues, Alex Zhou +3

In this work we propose long wave infrared (LWIR) imagery as a viable supporting modality for semantic segmentation using learning-based techniques. We first address the problem of…

cs.RO2019

Mine Tunnel Exploration using Multiple Quadrupedal Robots

Ian D. Miller, Fernando Cladera, Anthony Cowley +11

Robotic exploration of underground environments is a particularly challenging problem due to communication, endurance, and traversability constraints which necessitate high degrees…

cs.CV2019

MAVNet: an Effective Semantic Segmentation Micro-Network for MAV-based Tasks

Ty Nguyen, Shreyas S. Shivakumar, Ian D. Miller +9

Real-time semantic image segmentation on platforms subject to size, weight and power (SWaP) constraints is a key area of interest for air surveillance and inspection. In this work,…

cs.CV2019

DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance

Yilun Zhang, Ty Nguyen, Ian D. Miller +4

Depth estimation is an important capability for autonomous vehicles to understand and reconstruct 3D environments as well as avoid obstacles during the execution. Accurate depth se…