Object-oriented SLAM using Quadrics and Symmetry Properties for Indoor Environments
This work addresses a specific bottleneck in object-level SLAM for indoor robotics, offering incremental improvements in accuracy and speed.
This paper tackles the observability problem in quadric-based SLAM for indoor mobile robots by fusing object detection and point cloud data with symmetry properties, resulting in improved accuracy and convergence speed in quadric reconstruction compared to state-of-the-art methods.
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.