Abstract-In this paper, we present a novel benchmark for the evaluation of RGB-D SLAM systems. We recorded a large set of image sequences from a Microsoft Kinect with highly accurate and time-synchronized ground truth camera poses from a motion capture system. The sequences contain both the color and depth images in full sensor resolution (640 × 480) at video frame rate (30 Hz). The ground-truth trajectory was obtained from a motion-capture system with eight high-speed tracking cameras (100 Hz). The dataset consists of 39 sequences that were recorded in an office environment and an industrial hall. The dataset covers a large variety of scenes and camera motions. We provide sequences for debugging with slow motions as well as longer trajectories with and without loop closures. Most sequences were recorded from a handheld Kinect with unconstrained 6-DOF motions but we also provide sequences from a Kinect mounted on a Pioneer 3 robot that was manually navigated through a cluttered indoor environment. To stimulate the comparison of different approaches, we provide automatic evaluation tools both for the evaluation of drift of visual odometry systems and the global pose error of SLAM systems. The benchmark website [1] contains all data, detailed descriptions of the scenes, specifications of the data formats, sample code, and evaluation tools.

Abstract-In this paper, we propose a dense visual SLAM method for RGB-D cameras that minimizes both the photometric and the depth error over all pixels. In contrast to sparse, feature-based methods, this allows us to better exploit the available information in the image data which leads to higher pose accuracy. Furthermore, we propose an entropy-based similarity measure for keyframe selection and loop closure detection. From all successful matches, we build up a graph that we optimize using the g2o framework. We evaluated our approach extensively on publicly available benchmark datasets, and found that it performs well in scenes with low texture as well as low structure. In direct comparison to several stateof-the-art methods, our approach yields a significantly lower trajectory error. We release our software as open-source.

The self-diffusion coefficient of a series of DNA fragments ranging from 280 to 5386 bases has been measured by fluorescence recovery after photobleaching after thermal denaturation in 8 M urea. The total persistence length p of single-stranded DNAs and its variation in ionic strength down to 10-3 M has been deduced. The importance of the value of p versus the pore size a and contour length L of the DNA in the optimization of sequencing by gel electrophoresis is emphasized.

In this paper, we present a novel mapping system that robustly generates highly accurate 3-D maps using an RGB-D camera. Our approach requires no further sensors or odometry. With the availability of low-cost and light-weight RGB-D sensors such as the Microsoft Kinect, our approach applies to small domestic robots such as vacuum cleaners, as well as flying robots such as quadrocopters. Furthermore, our system can also be used for free-hand reconstruction of detailed 3-D models. In addition to the system itself, we present a thorough experimental evaluation on a publicly available benchmark dataset. We analyze and discuss the influence of several parameters such as the choice of the feature descriptor, the number of visual features, and validation methods. The results of the experiments demonstrate that our system can robustly deal with challenging scenarios such as fast camera motions and feature-poor environments while being fast enough for online operation. Our system is fully available as open source and has already been widely adopted by the robotics community. Index Terms-Localization, mapping, open source, RGB-D, simultaneous localization and mapping (SLAM).1552-3098 © 2013 IEEE

Abstract-We present an approach to simultaneous localization and mapping (SLAM) for RGB-D cameras like the Microsoft Kinect. Our system concurrently estimates the trajectory of a hand-held Kinect and generates a dense 3D model of the environment. We present the key features of our approach and evaluate its performance thoroughly on a recently published dataset, including a large set of sequences of different scenes with varying camera speeds and illumination conditions. In particular, we evaluate the accuracy, robustness, and processing time for three different feature descriptors (SIFT, SURF, and ORB). The experiments demonstrate that our system can robustly deal with difficult data in common indoor scenarios while being fast enough for online operation. Our system is fully available as open-source.

Abstract-The goal of our work is to provide a fast and accurate method to estimate the camera motion from RGB-D images. Our approach registers two consecutive RGB-D frames directly upon each other by minimizing the photometric error. We estimate the camera motion using non-linear minimization in combination with a coarse-to-fine scheme. To allow for noise and outliers in the image data, we propose to use a robust error function that reduces the influence of large residuals. Furthermore, our formulation allows for the inclusion of a motion model which can be based on prior knowledge, temporal filtering, or additional sensors like an IMU. Our method is attractive for robots with limited computational resources as it runs in real-time on a single CPU core and has a small, constant memory footprint. In an extensive set of experiments carried out both on a benchmark dataset and synthetic data, we demonstrate that our approach is more accurate and robust than previous methods. We provide our software under an open source license.

We propose a fundamentally novel approach to real-time visual odometry for a monocular camera. It allows to benefit from the simplicity and accuracy of dense trackingwhich does not depend on visual features -while running in real-time on a CPU. The key idea is to continuously estimate a semi-dense inverse depth map for the current frame, which in turn is used to track the motion of the camera using dense image alignment. More specifically, we estimate the depth of all pixels which have a non-negligible image gradient. Each estimate is represented as a Gaussian probability distribution over the inverse depth. We propagate this information over time, and update it with new measurements as new images arrive. In terms of tracking accuracy and computational speed, the proposed method compares favorably to both state-of-the-art dense and feature-based visual odometry and SLAM algorithms. As our method runs in real-time on a CPU, it is of large practical value for robotics and augmented reality applications. Towards Dense Monocular Visual OdometryTracking a hand-held camera and recovering the threedimensional structure of the environment in real-time is among the most prominent challenges in computer vision. In the last years, dense approaches to these challenges have become increasingly popular: Instead of operating solely on visual feature positions, they reconstruct and track on the whole image using a surface-based map and thereby are fundamentally different from feature-based approaches. Yet, these methods are to date either not real-time capable on standard CPUs [11,15,17] or require direct depth measurements from the sensor [7], making them unsuitable for many practical applications.In this paper, we propose a novel semi-dense visual odometry approach for a monocular camera, which combines the accuracy and robustness of dense approaches with the efficiency of feature-based methods. Further, it computes highly accurate semi-dense depth maps from the monocular images, providing rich information about the 3D * This work was supported by the ERC Starting Grant ConvexVision and the DFG project Mapping on Demand far close Figure 1. Semi-Dense Monocular Visual Odometry: Our approach works on a semi-dense inverse depth map and combines the accuracy and robustness of dense visual SLAM methods with the efficiency of feature-based techniques. Left: video frame, Right: color-coded semi-dense depth map, which consists of depth estimates in all image regions with sufficient structure. structure of the environment. We use the term visual odometry as supposed to SLAM, as -for simplicity -we deliberately maintain only information about the currently visible scene, instead of building a global world-model. Related WorkFeature-based monocular SLAM. In all feature-based methods (such as [4,8]), tracking and mapping consists of two separate steps: First, discrete feature observations (i.e., their locations in the image) are extracted and matched to each other. Second, the camera and the full feature poses are calculated from...

3D scans of indoor environments suffer from sensor occlusions, leaving 3D reconstructions with highly incomplete 3D geometry (left). We propose a novel data-driven approach based on fully-convolutional neural networks that transforms incomplete signed distance functions (SDFs) into complete meshes at unprecedented spatial extents (middle). In addition to scene completion, our approach infers semantic class labels even for previously missing geometry (right). Our approach outperforms existing approaches both in terms of completion and semantic labeling accuracy by a significant margin. AbstractWe introduce ScanComplete, a novel data-driven approach for taking an incomplete 3D scan of a scene as input and predicting a complete 3D model along with per-voxel semantic labels. The key contribution of our method is its ability to handle large scenes with varying spatial extent, managing the cubic growth in data size as scene size increases. To this end, we devise a fully-convolutional generative 3D CNN model whose filter kernels are invariant to the overall scene size. The model can be trained on scene subvolumes but deployed on arbitrarily large scenes at test time. In addition, we propose a coarse-to-fine inference strategy in order to produce high-resolution output while also leveraging large input context sizes. In an extensive series of experiments, we carefully evaluate different model design choices, considering both deterministic and probabilistic models for completion and semantic inference. Our results show that we outperform other methods not only in the size of the environments handled and processing efficiency, but also with regard to completion quality and semantic segmentation performance by a significant margin.