Semantic Relational Object Tracking

Abstract

This paper addresses the topic of semantic world modeling by conjoining probabilistic reasoning and object anchoring. The proposed approach uses a so-called bottom-up object anchoring method that relies on the rich continuous data from perceptual sensor data. A novel anchoring matching function method learns to maintain object entities in space and time and is validated using a large set of trained humanly annotated ground truth data of real-world objects. For more complex scenarios, a high-level probabilistic object tracker has been integrated with the anchoring framework and handles the tracking of occluded objects via reasoning about the state of unobserved objects. We demonstrate the performance of our integrated approach through scenarios such as the shell game scenario, where we illustrate how anchored objects are retained by preserving relations through probabilistic reasoning.

References in corpus (2)

• Inference and learning in probabilistic logic programs using weighted Boolean formulas
• The Magic of Logical Inference in Probabilistic Programming

Cited by in corpus (7)

• Development and evaluation of automated localisation and reconstruction of all fruits on tomato plants in a greenhouse based on multi-view perception and 3D multi-object tracking
• MOT-DETR: 3D Single Shot Detection and Tracking with Transformers to build 3D representations for Agro-Food Robots
• MinkSORT: A 3D deep feature extractor using sparse convolutions to improve 3D multi-object tracking in greenhouse tomato plants
• Semirings for Probabilistic and Neuro-Symbolic Logic Programming
• Boosting Object Representation Learning via Motion and Object Continuity
• SAILOR: Perceptual Anchoring For Robotic Cognitive Architectures
• A comparison between single-stage and two-stage 3D tracking algorithms for greenhouse robotics