Mina Henein

Zena Assaad, Mina Henein

The rapid development of new software and algorithms, fueled by the immense amount of data available, has made the shelf life of software products a lot shorter. With a rough estimate of more than 40,000 new software projects developed every day, it is becoming quicker and cheaper to abandon old software and acquire new software that meets rapidly changing needs and demands. What happens to software that is abandoned and what consequences may arise from 'throwaway' culture (Cooper, 2005) are still open questions. This paper will explore the systems engineering concept of end-of-life for software, it will highlight the gaps in existing software engineering practices, it will bring forward examples of software that has been abandoned in an attempt to decommission and it will explore the repercussions of abandoned software artefacts. A proposed way forward for addressing the identified research gaps is also detailed.

Jun Zhang, Mina Henein, Robert Mahony et al.

Combining Simultaneous Localisation and Mapping (SLAM) estimation and dynamic scene modelling can highly benefit robot autonomy in dynamic environments. Robot path planning and obstacle avoidance tasks rely on accurate estimations of the motion of dynamic objects in the scene. This paper presents VDO-SLAM, a robust visual dynamic object-aware SLAM system that exploits semantic information to enable accurate motion estimation and tracking of dynamic rigid objects in the scene without any prior knowledge of the objects' shape or geometric models. The proposed approach identifies and tracks the dynamic objects and the static structure in the environment and integrates this information into a unified SLAM framework. This results in highly accurate estimates of the robot's trajectory and the full SE(3) motion of the objects as well as a spatiotemporal map of the environment. The system is able to extract linear velocity estimates from objects' SE(3) motion providing an important functionality for navigation in complex dynamic environments. We demonstrate the performance of the proposed system on a number of real indoor and outdoor datasets and the results show consistent and substantial improvements over the state-of-the-art algorithms. An open-source version of the source code is available.

Yuheng Qiu, Chen Wang, Wenshan Wang et al.

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 on the same rigid part follow the same motion. In particular, we present AirDOS, a dynamic object-aware system that introduces rigidity and motion constraints to model articulated objects. By jointly optimizing the camera pose, object motion, and the object 3D structure, we can rectify the camera pose estimation, preventing tracking loss, and generate 4D spatio-temporal maps for both dynamic objects and static scenes. Experiments show that our algorithm improves the robustness of visual SLAM algorithms in challenging crowded urban environments. To the best of our knowledge, AirDOS is the first dynamic object-aware SLAM system demonstrating that camera pose estimation can be improved by incorporating dynamic articulated objects.

Jun Zhang, Mina Henein, Robert Mahony et al.

The problem of tracking self-motion as well as motion of objects in the scene using information from a camera is known as multi-body visual odometry and is a challenging task. This paper proposes a robust solution to achieve accurate estimation and consistent track-ability for dynamic multi-body visual odometry. A compact and effective framework is proposed leveraging recent advances in semantic instance-level segmentation and accurate optical flow estimation. A novel formulation, jointly optimizing SE(3) motion and optical flow is introduced that improves the quality of the tracked points and the motion estimation accuracy. The proposed approach is evaluated on the virtual KITTI Dataset and tested on the real KITTI Dataset, demonstrating its applicability to autonomous driving applications. For the benefit of the community, we make the source code public.

Mina Henein, Jun Zhang, Robert Mahony et al.

The static world assumption is standard in most simultaneous localisation and mapping (SLAM) algorithms. Increased deployment of autonomous systems to unstructured dynamic environments is driving a need to identify moving objects and estimate their velocity in real-time. Most existing SLAM based approaches rely on a database of 3D models of objects or impose significant motion constraints. In this paper, we propose 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. The algorithm generates a map of dynamic and static structure and has the ability to extract velocities of rigid moving objects in the scene. Its performance is demonstrated on simulated, synthetic and real-world datasets.

Mina Henein, Gerard Kennedy, Viorela Ila et al.

Accurate estimation of the environment structure simultaneously with the robot pose is a key capability of autonomous robotic vehicles. Classical simultaneous localization and mapping (SLAM) algorithms rely on the static world assumption to formulate the estimation problem, however, the real world has a significant amount of dynamics that can be exploited for a more accurate localization and versatile representation of the environment. In this paper we propose a technique to integrate the motion of dynamic objects into the SLAM estimation problem, without the necessity of estimating the pose or the geometry of the objects. To this end, we introduce a novel representation of the pose change of rigid bodies in motion and show the benefits of integrating such information when performing SLAM in dynamic environments. Our experiments show consistent improvement in robot localization and mapping accuracy when using a simple constant motion assumption, even for objects whose motion slightly violates this assumption.