# Multi-modal Tracking for Object based SLAM

arXiv: https://arxiv.org/abs/1603.04117 · HTML: https://mlsanity.com/paper/1603.04117/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2016.

## Built on by (most cited first, 1 of 1)

- [PoseKernelLifter: Metric Lifting of 3D Human Pose using Sound](https://mlsanity.com/paper/2112.00216/) (2021)

## Abstract (arXiv)

We present an on-line 3D visual object tracking framework for monocular cameras by incorporating spatial knowledge and uncertainty from semantic mapping along with high frequency measurements from visual odometry. Using a combination of vision and odometry that are tightly integrated we can increase the overall performance of object based tracking for semantic mapping. We present a framework for integration of the two data-sources into a coherent framework through information based fusion/arbitration. We demonstrate the framework in the context of OmniMapper[1] and present results on 6 challenging sequences over multiple objects compared to data obtained from a motion capture systems. We are able to achieve a mean error of 0.23m for per frame tracking showing 9% relative error less than state of the art tracker.