A Survey on Joint Object Detection and Pose Estimation using Monocular Vision

In this survey we present a complete landscape of joint object detection and pose estimation methods that use monocular vision. Descriptions of traditional approaches that involve descriptors or models and various estimation methods have been provided. These descriptors or models include chordiograms, shape-aware deformable parts model, bag of boundaries, distance transform templates, natural 3D markers and facet features whereas the estimation methods include iterative clustering estimation, probabilistic networks and iterative genetic matching. Hybrid approaches that use handcrafted feature extraction followed by estimation by deep learning methods have been outlined. We have investigated and compared, wherever possible, pure deep learning based approaches (single stage and multi stage) for this problem. Comprehensive details of the various accuracy measures and metrics have been illustrated. For the purpose of giving a clear overview, the characteristics of relevant datasets are discussed. The trends that prevailed from the infancy of this problem until now have also been highlighted.Comment: Accepted at the International Joint Conference on Computer Vision and Pattern Recognition (CCVPR) 201

Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot

Mobile manipulation tasks are one of the key challenges in the field of search and rescue (SAR) robotics requiring robots with flexible locomotion and manipulation abilities. Since the tasks are mostly unknown in advance, the robot has to adapt to a wide variety of terrains and workspaces during a mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and an anthropomorphic upper body to carry out complex tasks in environments too dangerous for humans. Due to its high number of degrees of freedom, controlling the robot with direct teleoperation approaches is challenging and exhausting. Supervised autonomy approaches are promising to increase quality and speed of control while keeping the flexibility to solve unknown tasks. We developed a set of operator assistance functionalities with different levels of autonomy to control the robot for challenging locomotion and manipulation tasks. The integrated system was evaluated in disaster response scenarios and showed promising performance.Comment: In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 201

Polarimetric Pose Prediction

Light has many properties that vision sensors can passively measure. Colour-band separated wavelength and intensity are arguably the most commonly used for monocular 6D object pose estimation. This paper explores how complementary polarisation information, i.e. the orientation of light wave oscillations, influences the accuracy of pose predictions. A hybrid model that leverages physical priors jointly with a data-driven learning strategy is designed and carefully tested on objects with different levels of photometric complexity. Our design significantly improves the pose accuracy compared to state-of-the-art photometric approaches and enables object pose estimation for highly reflective and transparent objects. A new multi-modal instance-level 6D object pose dataset with highly accurate pose annotations for multiple objects with varying photometric complexity is introduced as a benchmark.Comment: Accepted at ECCV 2022; 25 pages (14 main paper + References + 7 Appendix

Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packing

Robotic picking from cluttered bins is a demanding task, for which Amazon Robotics holds challenges. The 2017 Amazon Robotics Challenge (ARC) required stowing items into a storage system, picking specific items, and packing them into boxes. In this paper, we describe the entry of team NimbRo Picking. Our deep object perception pipeline can be quickly and efficiently adapted to new items using a custom turntable capture system and transfer learning. It produces high-quality item segments, on which grasp poses are found. A planning component coordinates manipulation actions between two robot arms, minimizing execution time. The system has been demonstrated successfully at ARC, where our team reached second places in both the picking task and the final stow-and-pick task. We also evaluate individual components.Comment: In: Proceedings of the International Conference on Robotics and Automation (ICRA) 201

Perception systems for robust autonomous navigation in natural environments

2022 Spring.Includes bibliographical references.As assistive robotics continues to develop thanks to the rapid advances of artificial intelligence, smart sensors, Internet of Things, and robotics, the industry began introducing robots to perform various functions that make humans' lives more comfortable and enjoyable. While the principal purpose of deploying robots has been productivity enhancement, their usability has widely expanded. Examples include assisting people with disabilities (e.g., Toyota's Human Support Robot), providing driver-less transportation (e.g., Waymo's driver-less cars), and helping with tedious house chores (e.g., iRobot). The challenge in these applications is that the robots have to function appropriately under continuously changing environments, harsh real-world conditions, deal with significant amounts of noise and uncertainty, and operate autonomously without the intervention or supervision of an expert. To meet these challenges, a robust perception system is vital. This dissertation casts light on the perception component of autonomous mobile robots and highlights their major capabilities, and analyzes the factors that affect their performance. In short, the developed approaches in this dissertation cover the following four topics: (1) learning the detection and identification of objects in the environment in which the robot is operating, (2) estimating the 6D pose of objects of interest to the robot, (3) studying the importance of the tracking information in the motion prediction module, and (4) analyzing the performance of three motion prediction methods, comparing their performances, and highlighting their strengths and weaknesses. All techniques developed in this dissertation have been implemented and evaluated on popular public benchmarks. Extensive experiments have been conducted to analyze and validate the properties of the developed methods and demonstrate this dissertation's conclusions on the robustness, performance, and utility of the proposed approaches for intelligent mobile robots