SLAM with objects using a nonparametric pose graph

Mapping and self-localization in unknown environments are fundamental capabilities in many robotic applications. These tasks typically involve the identification of objects as unique features or landmarks, which requires the objects both to be detected and then assigned a unique identifier that can be maintained when viewed from different perspectives and in different images. The data association and simultaneous localization and mapping (SLAM) problems are, individually, well-studied in the literature. But these two problems are inherently tightly coupled, and that has not been well-addressed. Without accurate SLAM, possible data associations are combinatorial and become intractable easily. Without accurate data association, the error of SLAM algorithms diverge easily. This paper proposes a novel nonparametric pose graph that models data association and SLAM in a single framework. An algorithm is further introduced to alternate between inferring data association and performing SLAM. Experimental results show that our approach has the new capability of associating object detections and localizing objects at the same time, leading to significantly better performance on both the data association and SLAM problems than achieved by considering only one and ignoring imperfections in the other.

Authors

• Beipeng Mu
• Shih-Yuan Liu
• Liam Paull
• John J. Leonard
• Jonathan P. How

Keywords

• Simultaneous localization and mapping
• Object detection
• Three-dimensional displays
• Machine learning
• Robustness
• Proposals
• Pose Graph
• Unique Identifier
• Robotic Applications
• Unknown Environment
• Deep Learning
• Objective Measures
• Point Cloud
• Simulated Datasets
• Bounding Box
• Object Classification
• Object Location
• Number Of Objects
• Depth Images
• Gibbs Sampling
• Central Objective
• Class Instances
• Robot Pose
• Factor Graph
• Laser Ranging
• Dirichlet Distribution
• Dirichlet Process
• Robot Trajectory
• False Positive
• Log-likelihood
• Object Position
• Object Instances