# OrcVIO: Object residual constrained Visual-Inertial Odometry

arXiv: https://arxiv.org/abs/2007.15107 · HTML: https://mlsanity.com/paper/2007.15107/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2101.06894/) (2021)
- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments](https://mlsanity.com/paper/2103.10400/) (2021)
- [Visual-Inertial Multi-Instance Dynamic SLAM with Object-level Relocalisation](https://mlsanity.com/paper/2208.04274/) (2022)
- [Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency](https://mlsanity.com/paper/2304.05146/) (2023)

## Abstract (arXiv)

Introducing object-level semantic information into simultaneous localization and mapping (SLAM) system is critical. It not only improves the performance but also enables tasks specified in terms of meaningful objects. This work presents OrcVIO, for visual-inertial odometry tightly coupled with tracking and optimization over structured object models. OrcVIO differentiates through semantic feature and bounding-box reprojection errors to perform batch optimization over the pose and shape of objects. The estimated object states aid in real-time incremental optimization over the IMU-camera states. The ability of OrcVIO for accurate trajectory estimation and large-scale object-level mapping is evaluated using real data.