The PASCAL Visual Object Classes Challenge

A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very little data is available &#x2013; current datasets cover a small range of scene views and have limited semantic annotations. To address this issue, we introduce ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations. To collect this data, we designed an easy-to-use and scalable RGB-D capture system that includes automated surface reconstruction and crowdsourced semantic annotation. We show that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks, including 3D object classification, semantic voxel labeling, and CAD model retrieval.

/pdf/scannet-richly-annotated-3d-reconstructions-of-indoor-scenes-37ihdazbor.pdf

ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.

/pdf/octomap-an-efficient-probabilistic-3d-mapping-framework-3dp42nnofi.pdf

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

Simultaneous localization and mapping (SLAM) consists in the concurrent construction of a model of the environment (the map ), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications and witnessing a steady transition of this technology to industry. We survey the current state of SLAM and consider future directions. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors’ take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?

/pdf/past-present-and-future-of-simultaneous-localization-and-27i1pn9khc.pdf

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Orientation estimation using low cost sensors is an important task for Micro Aerial Vehicles (MAVs) in order to obtain a good feedback for the attitude controller. The challenges come from the low accuracy and noisy data of the MicroElectroMechanical System (MEMS) technology, which is the basis of modern, miniaturized inertial sensors. In this article, we describe a novel approach to obtain an estimation of the orientation in quaternion form from the observations of gravity and magnetic field. Our approach provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. We separate the problems of finding the "tilt" quaternion and the heading quaternion in two sub-parts of our system. This procedure is the key for avoiding the impact of the magnetic disturbances on the roll and pitch components of the orientation when the sensor is surrounded by unwanted magnetic flux. We demonstrate the validity of our method first analytically and then empirically using simulated data. We propose a novel complementary filter for MAVs that fuses together gyroscope data with accelerometer and magnetic field readings. The correction part of the filter is based on the method described above and works for both IMU (Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity) sensors. We evaluate the effectiveness of the filter and show that it significantly outperforms other common methods, using publicly available datasets with ground-truth data recorded during a real flight experiment of a micro quadrotor helicopter.

/pdf/keeping-a-good-attitude-a-quaternion-based-orientation-2uoi13loy2.pdf

Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs

Robot 3D three-dimension path planning targets for finding an optimal and collision-free path in a 3D workspace while taking into account kinematic constraints including geometric, physical, and temporal constraints. The purpose of path planning, unlike motion planning which must be taken into consideration of dynamics, is to find a kinematically optimal path with the least time as well as model the environment completely. We discuss the fundamentals of these most successful robot 3D path planning algorithms which have been developed in recent years and concentrate on universally applicable algorithms which can be implemented in aerial robots, ground robots, and underwater robots. This paper classifies all the methods into five categories based on their exploring mechanisms and proposes a category, called multifusion based algorithms. For all these algorithms, they are analyzed from a time efficiency and implementable area perspective. Furthermore a comprehensive applicable analysis for each kind of method is presented after considering their merits and weaknesses.

/pdf/survey-of-robot-3d-path-planning-algorithms-1a99fixnk9.pdf

Survey of Robot 3D Path Planning Algorithms

We describe CHISEL: a system for real-time housescale (300 square meter or more) dense 3D reconstruction onboard a Google Tango [1] mobile device by using a dynamic spatially-hashed truncated signed distance field[2] for mapping, and visual-inertial odometry for localization. By aggressively culling parts of the scene that do not contain surfaces, we avoid needless computation and wasted memory. Even under very noisy conditions, we produce high-quality reconstructions through the use of space carving. We are able to reconstruct and render very large scenes at a resolution of 2-3 cm in real time on a mobile device without the use of GPU computing. The user is able to view and interact with the reconstruction in real-time through an intuitive interface. We provide both qualitative and quantitative results on publicly available RGB-D datasets [3], and on datasets collected in real-time from two devices.

http://www.roboticsproceedings.org/rss11/p40.pdf

Chisel: Real Time Large Scale 3D Reconstruction Onboard a Mobile Device using Spatially Hashed Signed Distance Fields

An RGB-D camera is a sensor which outputs color and depth and information about the scene it observes. In this paper, we present a real-time visual odometry and mapping system for RGB-D cameras. The system runs at frequencies of 30Hz and higher in a single thread on a desktop CPU with no GPU acceleration required. We recover the unconstrained 6-DoF trajectory of a moving camera by aligning sparse features observed in the current RGB-D image against a model of previous features. The model is persistent and dynamically updated from new observations using a Kalman Filter. We formulate a novel uncertainty measure for sparse RGD-B features based on a Gaussian mixture model for the filtering stage. Our registration algorithm is capable of closing small-scale loops in indoor environments online without any additional SLAM back-end techniques.

Fast visual odometry and mapping from RGB-D data

3D path planning of unmanned aerial vehicle (UAV) targets at finding an optimal and collision free path in a 3D cluttered environment while taking into account the geometric, physical and temporal constraints. Although a lot of works have been done to solve UAV 3D path planning problem, there lacks a comprehensive survey on this topic, let alone the recently published works that focus on this field. This paper analyses the most successful UAV 3D path planning algorithms that developed in recent years. This paper classifies the UAV 3D path planning methods into five categories, sampling-based algorithms, node-based algorithms, mathematical model based algorithms, Bio-inspired algorithms, and multi-fusion based algorithms. For each category a critical analysis and comparison is given. Furthermore a comprehensive applicable analysis for each kind of method is presented after considering its working mechanism and time complexity.

Jizhong Xiao

Papers

Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs

Survey of Robot 3D Path Planning Algorithms

Chisel: Real Time Large Scale 3D Reconstruction Onboard a Mobile Device using Spatially Hashed Signed Distance Fields

Fast visual odometry and mapping from RGB-D data

A literature review of UAV 3D path planning