# Semantic Mapping with Simultaneous Object Detection and Localization

Source: [Semantic Mapping with Simultaneous Object Detection and Localization](<https://www.sota2.com/research/paper/68fa64f91e209131cea077da>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:15:52.693Z

Authors: Zhen Zeng, Yunwen Zhou, Odest Chadwicke Jenkins, Karthik Desingh

Published: 2018-10-26T20:49:22.000Z

## Abstract

We present a filtering-based method for semantic mapping to simultaneously detect objects and localize their 6 degree-of-freedom pose. For our method, called Contextual Temporal Mapping (or CT-Map), we represent the semantic map as a belief over object classes and poses across an observed scene. Inference for the semantic mapping problem is then modeled in the form of a Conditional Random Field (CRF). CT-Map is a CRF that considers two forms of relationship potentials to account for contextual relations between objects and temporal consistency of object poses, as well as a measurement potential on observations. A particle filtering algorithm is then proposed to perform inference in the CT-Map model. We demonstrate the efficacy of the CT-Map method with a Michigan Progress Fetch robot equipped with a RGB-D sensor. Our results demonstrate that the particle filtering based inference of CT-Map provides improved object detection and pose estimation with respect to baseline methods that treat observations as independent samples of a scene.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1810.11525>)

[PDF](<https://arxiv.org/pdf/1810.11525.pdf>)