6,669 research outputs found
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments
Existing simultaneous localization and mapping (SLAM) algorithms are not
robust in challenging low-texture environments because there are only few
salient features. The resulting sparse or semi-dense map also conveys little
information for motion planning. Though some work utilize plane or scene layout
for dense map regularization, they require decent state estimation from other
sources. In this paper, we propose real-time monocular plane SLAM to
demonstrate that scene understanding could improve both state estimation and
dense mapping especially in low-texture environments. The plane measurements
come from a pop-up 3D plane model applied to each single image. We also combine
planes with point based SLAM to improve robustness. On a public TUM dataset,
our algorithm generates a dense semantic 3D model with pixel depth error of 6.2
cm while existing SLAM algorithms fail. On a 60 m long dataset with loops, our
method creates a much better 3D model with state estimation error of 0.67%.Comment: International Conference on Intelligent Robots and Systems (IROS)
201
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots
Safety is paramount for mobile robotic platforms such as self-driving cars
and unmanned aerial vehicles. This work is devoted to a task that is
indispensable for safety yet was largely overlooked in the past -- detecting
obstacles that are of very thin structures, such as wires, cables and tree
branches. This is a challenging problem, as thin objects can be problematic for
active sensors such as lidar and sonar and even for stereo cameras. In this
work, we propose to use video sequences for thin obstacle detection. We
represent obstacles with edges in the video frames, and reconstruct them in 3D
using efficient edge-based visual odometry techniques. We provide both a
monocular camera solution and a stereo camera solution. The former incorporates
Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter
enjoys a novel, purely vision-based solution. Experiments demonstrated that the
proposed methods are fast and able to detect thin obstacles robustly and
accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio
- …