AirDOS: Dynamic SLAM benefits from Articulated Objects

  title={AirDOS: Dynamic SLAM benefits from Articulated Objects},
  author={Yuheng Qiu and Chen Wang and Wenshan Wang and Mina Henein and Sebastian A. Scherer},
  journal={2022 International Conference on Robotics and Automation (ICRA)},
  • Yuheng Qiu, Chen Wang, S. Scherer
  • Published 21 September 2021
  • Computer Science
  • 2022 International Conference on Robotics and Automation (ICRA)
Dynamic Object-aware SLAM (DOS) exploits object-level information to enable robust motion estimation in dynamic environments. Existing methods mainly focus on identifying and excluding dynamic objects from the optimization. In this paper, we show that feature-based visual SLAM systems can also benefit from the presence of dynamic articulated objects by taking advantage of two observations: (1) The 3D structure of each rigid part of articulated object remains consistent over time; (2) The points… 

Figures and Tables from this paper

BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Tracking

It is demonstrated that BodySLAM improves estimates of all human body parameters and camera poses when compared to estimating these separately, and introduces a novel human motion model to constrain sequential body postures.

DytanVO: Joint Refinement of Visual Odometry and Motion Segmentation in Dynamic Environments

—Learning-based visual odometry (VO) algorithms achieve remarkable performance on common static scenes, benefiting from high-capacity models and massive annotated data, but tend to fail in dynamic,

Improved Visual SLAM Using Semantic Segmentation and Layout Estimation

Inspired by the human brain’s navigation techniques, two machine learning techniques are incorporated into a VSLAM solution: semantic segmentation and layout estimation to imitate human abilities to map new environments.

MLO: Multi-Object Tracking and Lidar Odometry in Dynamic Environment

The experiment results show that the ego localization accuracy of MLO is better than A-LOAM system in highly dynamic, unstructured, and unknown semantic scenes and the multi-object tracking method with semantic-geometry fusion also has apparent advantages in accuracy and tracking robustness compared with the single method.



VDO-SLAM: A Visual Dynamic Object-aware SLAM System

VDO-SLAM is presented, a robust object-aware dynamic SLAM system that exploits semantic information to enable motion estimation of rigid objects in the scene without any prior knowledge of the objects shape or motion models resulting in accurate robot pose and spatio-temporal map estimation.

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.

TartanAir: A Dataset to Push the Limits of Visual SLAM

The goal is to push the limits of Visual SLAM algorithms in the real world by providing a challenging benchmark for testing new methods, while also using a large diverse training data for learning-based methods.

CubeSLAM: Monocular 3-D Object SLAM

The SLAM method achieves the state-of-the-art monocular camera pose estimation and at the same time, improves the 3-D object detection accuracy.

ORB-SLAM: A Versatile and Accurate Monocular SLAM System

A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation.

Exploiting Rigid Body Motion for SLAM in Dynamic Environments

This paper proposes a technique to integrate the motion of dynamic objects into a Simultaneous Localisation and Mapping (SLAM) algorithm without the need to know a-priori or model the geometry of the object, or even to explicitly estimate the pose of theobject.

Towards Real-time Semantic RGB-D SLAM in Dynamic Environments

This paper proposes a real-time semantic RGBD SLAM system for dynamic environments that is capable of detecting both known and unknown moving objects, and one of the first semantic RGB-DSLAM systems that run in real- time on a low-power embedded platform and provide high localization accuracy in dynamic environments.

Dynamic SLAM: The Need For Speed

  • M. HeneinJun ZhangR. MahonyV. Ila
  • Computer Science
    2020 IEEE International Conference on Robotics and Automation (ICRA)
  • 2020
This paper proposes a new feature-based, model-free, object-aware dynamic SLAM algorithm that exploits semantic segmentation to allow estimation of motion of rigid objects in a scene without the need to estimate the object poses or have any prior knowledge of their 3D models.

3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans

This is the first paper that reconciles visual-inertial SLAM and dense human mesh tracking and can have a profound impact on planning and decision-making, human-robot interaction, long-term autonomy, and scene prediction.