2 research outputs found
An Orientation Factor for Object-Oriented SLAM
Current approaches to object-oriented SLAM lack the ability to incorporate
prior knowledge of the scene geometry, such as the expected global orientation
of objects. We overcome this limitation by proposing a geometric factor that
constrains the global orientation of objects in the map, depending on the
objects' semantics. This new geometric factor is a first example of how
semantics can inform and improve geometry in object-oriented SLAM. We implement
the geometric factor for the recently proposed QuadricSLAM that represents
landmarks as dual quadrics. The factor probabilistically models the quadrics'
major axes to be either perpendicular to or aligned with the direction of
gravity, depending on their semantic class. Our experiments on simulated and
real-world datasets show that using the proposed factors to incorporate prior
knowledge improves both the trajectory and landmark quality.Comment: Submitted to ICRA 2019, under revie
Object-oriented SLAM using Quadrics and Symmetry Properties for Indoor Environments
Aiming at the application environment of indoor mobile robots, this paper
proposes a sparse object-level SLAM algorithm based on an RGB-D camera. A
quadric representation is used as a landmark to compactly model objects,
including their position, orientation, and occupied space. The state-of-art
quadric-based SLAM algorithm faces the observability problem caused by the
limited perspective under the plane trajectory of the mobile robot. To solve
the problem, the proposed algorithm fuses both object detection and point cloud
data to estimate the quadric parameters. It finishes the quadric initialization
based on a single frame of RGB-D data, which significantly reduces the
requirements for perspective changes. As objects are often observed locally,
the proposed algorithm uses the symmetrical properties of indoor artificial
objects to estimate the occluded parts to obtain more accurate quadric
parameters. Experiments have shown that compared with the state-of-art
algorithm, especially on the forward trajectory of mobile robots, the proposed
algorithm significantly improves the accuracy and convergence speed of quadric
reconstruction. Finally, we made available an opensource implementation to
replicate the experiments.Comment: Submission to IROS 2020. Video: https://youtu.be/u9zRBp4TPI