Simultaneous Localization and Mapping using RGB-D cameras has been a fertile research topic in the latest decade, due to the suitability of such sensors for indoor robotics. In this paper we propose a direct RGB-D SLAM algorithm with state-of-the-art accuracy and robustness at a los cost. Our experiments in the RGB-D TUM dataset [34] effectively show a better accuracy and robustness in CPU real time than direct RGB-D SLAM systems that make use of the GPU. The key ingredients of our approach are mainly two. Firstly, the combination of a semi-dense photometric and dense geometric error for the pose tracking (see Figure 1), which we demonstrate to be the most accurate alternative. And secondly, a model of the multi-view constraints and their errors in the mapping and tracking threads, which adds extra information over other approaches. We release the open-source implementation of our approach 1 . The reader is referred to a video with our results 2 for a more illustrative visualization of its performance.
One of the first approaches for direct RGB-D odometry is KinectFusion @cite_7 , which uses only the depth channel D to estimate the odometry and a dense map and discards the RGB information. As its main limitations, it is restricted to small workspaces and will probably fail if the scene does not contain enough geometric structure.
@cite_7
1639700302 2126457740 2830339951 2058535340 2751489730 2963562098 2211874131 2336469227 2474281075 2295478803 2416791088 1972671602 2743552164 2156222070 1716229439 2586341064 2123432765 2794739174 2952174552 2775132987
In this paper we present a dense visual odometry system for RGB-D cameras performing both photometric and geometric error minimisation to estimate the camera motion between frames. Contrary to most works in the literature, we parametrise the geometric error by the inverse depth instead of the depth, which translates into a better fit of the distribution of the geometric error to the used robust cost functions. We also provide a unified evaluation under the same framework of different estimators and ways of computing the scale of the residuals which can be found spread along the related literature. For the comparison of our approach with state-of-the-art approaches we use the popular dataset from the TUM for RGB-D benchmarking. Our approach shows to be competitive with state-of-the-art methods in terms of drift in meters per second, even compared to methods performing loop closure too. When comparing to approaches performing pure odometry like ours, our method outperforms them in the majority of the tested datasets. Additionally we show that our approach is able to work in real time and we provide a qualitative evaluation on our own sequences showing a low drift in the 3D reconstructions. This paper describes extensions to the Kintinuous [1] algorithm for spatially extended KinectFusion, incorporating the following additions: (i) the integration of multiple 6DOF camera odometry estimation methods for robust tracking; (ii) a novel GPU-based implementation of an existing dense RGB-D visual odometry algorithm; (iii) advanced fused realtime surface coloring. These extensions are validated with extensive experimental results, both quantitative and qualitative, demonstrating the ability to build dense fully colored models of spatially extended environments for robotics and virtual reality applications while remaining robust against scenes with challenging sets of geometric and visual features. Monocular visual odometry approaches that purely rely on geometric cues are prone to scale drift and require sufficient motion parallax in successive frames for motion estimation and 3D reconstruction. In this paper, we propose to leverage deep monocular depth prediction to overcome limitations of geometry-based monocular visual odometry. To this end, we incorporate deep depth predictions into Direct Sparse Odometry (DSO) as direct virtual stereo measurements. For depth prediction, we design a novel deep network that refines predicted depth from a single image in a two-stage process. We train our network in a semi-supervised way on photoconsistency in stereo images and on consistency with accurate sparse depth reconstructions from Stereo DSO. Our deep predictions excel state-of-the-art approaches for monocular depth on the KITTI benchmark. Moreover, our Deep Virtual Stereo Odometry clearly exceeds previous monocular and deep-learning based methods in accuracy. It even achieves comparable performance to the state-of-the-art stereo methods, while only relying on a single camera. We introduce the Imperial College London and National University of Ireland Maynooth (ICL-NUIM) dataset for the evaluation of visual odometry, 3D reconstruction and SLAM algorithms that typically use RGB-D data. We present a collection of handheld RGB-D camera sequences within synthetically generated environments. RGB-D sequences with perfect ground truth poses are provided as well as a ground truth surface model that enables a method of quantitatively evaluating the final map or surface reconstruction accuracy. Care has been taken to simulate typically observed real-world artefacts in the synthetic imagery by modelling sensor noise in both RGB and depth data. While this dataset is useful for the evaluation of visual odometry and SLAM trajectory estimation, our main focus is on providing a method to benchmark the surface reconstruction accuracy which to date has been missing in the RGB-D community despite the plethora of ground truth RGB-D datasets available. We propose Stereo Direct Sparse Odometry (Stereo DSO) as a novel method for highly accurate real-time visual odometry estimation of large-scale environments from stereo cameras. It jointly optimizes for all the model parameters within the active window, including the intrinsic extrinsic camera parameters of all keyframes and the depth values of all selected pixels. In particular, we propose a novel approach to integrate constraints from static stereo into the bundle adjustment pipeline of temporal multi-view stereo. Real-time optimization is realized by sampling pixels uniformly from image regions with sufficient intensity gradient. Fixed-baseline stereo resolves scale drift. It also reduces the sensitivities to large optical flow and to rolling shutter effect which are known shortcomings of direct image alignment methods. Quantitative evaluation demonstrates that the proposed Stereo DSO outperforms existing state-of-the-art visual odometry methods both in terms of tracking accuracy and robustness. Moreover, our method delivers a more precise metric 3D reconstruction than previous dense semi-dense direct approaches while providing a higher reconstruction density than feature-based methods. We propose Stereo Direct Sparse Odometry (Stereo DSO) as a novel method for highly accurate real-time visual odometry estimation of large-scale environments from stereo cameras. It jointly optimizes for all the model parameters within the active window, including the intrinsic extrinsic camera parameters of all keyframes and the depth values of all selected pixels. In particular, we propose a novel approach to integrate constraints from static stereo into the bundle adjustment pipeline of temporal multi-view stereo. Real-time optimization is realized by sampling pixels uniformly from image regions with sufficient intensity gradient. Fixed-baseline stereo resolves scale drift. It also reduces the sensitivities to large optical flow and to rolling shutter effect which are known shortcomings of direct image alignment methods. Quantitative evaluation demonstrates that the proposed Stereo DSO outperforms existing state-of-the-art visual odometry methods both in terms of tracking accuracy and robustness. Moreover, our method delivers a more precise metric 3D reconstruction than previous dense semi-dense direct approaches while providing a higher reconstruction density than feature-based methods. Lighting variation and uneven feature distribution are main challenges for indoor RGB-D visual odometry where color information is often combined with depth information. To meet the challenges, we fuse point and line features to form a robust odometry algorithm. Line features are abundant indoors and less sensitive to lighting change than points. We extract 3D points and lines from RGB-D data, analyze their measurement uncertainties, and compute camera motion using maximum likelihood estimation. We prove that fusing points and lines produces smaller motion estimate uncertainty than using either feature type alone. In experiments we compare our method with state-of-the-art methods including a keypoint-based approach and a dense visual odometry algorithm. Our method outperforms the counterparts under both constant and varying lighting conditions. Specifically, our method achieves an average translational error that is 34.9 smaller than the counterparts, when tested using public datasets. In this paper we present a novel semi-direct tracking and mapping (SDTAM) approach for RGB-D cameras which inherits the advantages of both direct and feature based methods, and consequently it achieves high efficiency, accuracy, and robustness. The input RGB-D frames are tracked with a direct method and keyframes are refined by minimizing a proposed measurement residual function which takes both geometric and depth information into account. A local optimization is performed to refine the local map while global optimization detects and corrects loops with the appearance based bag of words and a co-visibility weighted pose graph. Our method has higher accuracy on both trajectory tracking and surface reconstruction compared to state-of-the-art frame-to-frame or frame-model approaches. We test our system in challenging sequences with motion blur, fast pure rotation, and large moving objects, the results demonstrate it can still successfully obtain results with high accuracy. Furthermore, the proposed approach achieves real-time speed which only uses part of the CPU computation power, and it can be applied to embedded devices such as phones, tablets, or micro aerial vehicles (MAVs). Direct Sparse Odometry (DSO) is a visual odometry method based on a novel, highly accurate sparse and direct structure and motion formulation. It combines a fully direct probabilistic model (minimizing a photometric error) with consistent, joint optimization of all model parameters, including geometry-represented as inverse depth in a reference frame-and camera motion. This is achieved in real time by omitting the smoothness prior used in other direct methods and instead sampling pixels evenly throughout the images. Since our method does not depend on keypoint detectors or descriptors, it can naturally sample pixels from across all image regions that have intensity gradient, including edges or smooth intensity variations on essentially featureless walls. The proposed model integrates a full photometric calibration, accounting for exposure time, lens vignetting, and non-linear response functions. We thoroughly evaluate our method on three different datasets comprising several hours of video. The experiments show that the presented approach significantly outperforms state-of-the-art direct and indirect methods in a variety of real-world settings, both in terms of tracking accuracy and robustness. This letter presents a novel method for visual odometry estimation from a RGB-D camera. The camera motion is estimated by aligning a source to a target RGB-D frame using an intensity-assisted iterative closest point (ICP) algorithm. The proposed method differs from the conventional ICP in following aspects. 1) To reduce the computational cost, salient point selection is performed on the source frame, where only points that contain valuable information for registration are used. 2) To reduce the influence of outliers and noises, robust weighting function is proposed to weight corresponding pairs based on statistics of their spatial distances and intensity differences. 3) The obtained robust weighting function from 2) is used for correspondence estimation of the following ICP iteration. The proposed method runs in real-time with a single core CPU thread, hence it is suitable for robots with limited computation resources. The evaluation on TUM RGB-D benchmark shows that in the majority of the tested sequences, our proposed method outperforms state-of-the-art accuracy in terms of translational drift per second with a computation speed of 78 Hz. In this paper, we explore various aspects of fusing LIDAR and color imagery for pedestrian detection in the context of convolutional neural networks (CNNs), which have recently become state-of-art for many vision problems. We incorporate LIDAR by up-sampling the point cloud to a dense depth map and then extracting three features representing different aspects of the 3D scene. We then use those features as extra image channels. Specifically, we leverage recent work on HHA [9] (horizontal disparity, height above ground, and angle) representations, adapting the code to work on up-sampled LIDAR rather than Microsoft Kinect depth maps. We show, for the first time, that such a representation is applicable to up-sampled LIDAR data, despite its sparsity. Since CNNs learn a deep hierarchy of feature representations, we then explore the question: At what level of representation should we fuse this additional information with the original RGB image channels? We use the KITTI pedestrian detection dataset for our exploration. We first replicate the finding that region-CNNs (R-CNNs) [8] can outperform the original proposal mechanism using only RGB images, but only if fine-tuning is employed. Then, we show that: 1) using HHA features and RGB images performs better than RGB-only, even without any fine-tuning using large RGB web data, 2) fusing RGB and HHA achieves the strongest results if done late, but, under a parameter or computational budget, is best done at the early to middle layers of the hierarchical representation, which tend to represent midlevel features rather than low (e.g. edges) or high (e.g. object class decision) level features, 3) some of the less successful methods have the most parameters, indicating that increased classification accuracy is not simply a function of increased capacity in the neural network. RGB-D cameras (such as the Microsoft Kinect) are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. We present RGB-D Mapping, a full 3D mapping system that utilizes a novel joint optimization algorithm combining visual features and shape-based alignment. Visual and depth information are also combined for view-based loop-closure detection, followed by pose optimization to achieve globally consistent maps. We evaluate RGB-D Mapping on two large indoor environments, and show that it effectively combines the visual and shape information available from RGB-D cameras. Commercial RGB-D cameras provide the possibility of fast, accurate, and cost-effective 3-D scanning solution in a single package. These economical depth cameras provide several advantages over conventional depth sensors, such as sonars and lidars, in specific usage scenarios. In this paper, we analyze the performance of Kinect v2 time-of-flight camera while operating fully submerged underwater in a customized waterproof housing. Camera calibration has been performed for Kinect’s RGB and NIR cameras, and the effect of calibration on the generated 3-D mesh is discussed in detail. To overcome the effect of refraction of light due to the sensor housing and water, we propose a time-of-flight correction method and a fast, accurate and intuitive refraction correction method that can be applied to the acquired depth images, during 3-D mesh generation. Experimental results show that the Kinect v2 can acquire point cloud data up to 650 mm. The reconstruction results have been analyzed qualitatively and quantitatively, and confirm that the 3-D reconstruction of submerged objects at small distances is possible without the requirement of any external NIR light source. The proposed algorithms successfully generated 3-D mesh with a mean error of ±6 mm at a frame rate of nearly 10 fps. We acquired a large data set of RGB, IR and depth data from a submerged Kinect v2. The data set covers a large variety of objects scanned underwater and is publicly available for further use, along with the Kinect waterproof housing design and correction filter codes. The research is aimed toward small-scale research activities and economical solution for 3-D scanning underwater. Applications such as coral reef mapping and underwater SLAM in shallow waters for ROV’s can be a viable application area that can benefit from results achieved. Over the last decade, the availability of public image repositories and recognition benchmarks has enabled rapid progress in visual object category and instance detection. Today we are witnessing the birth of a new generation of sensing technologies capable of providing high quality synchronized videos of both color and depth, the RGB-D (Kinect-style) camera. With its advanced sensing capabilities and the potential for mass adoption, this technology represents an opportunity to dramatically increase robotic object recognition, manipulation, navigation, and interaction capabilities. In this paper, we introduce a large-scale, hierarchical multi-view object dataset collected using an RGB-D camera. The dataset contains 300 objects organized into 51 categories and has been made publicly available to the research community so as to enable rapid progress based on this promising technology. This paper describes the dataset collection procedure and introduces techniques for RGB-D based object recognition and detection, demonstrating that combining color and depth information substantially improves quality of results. In this paper we present an extension to the KinectFusion algorithm that permits dense mesh-based mapping of extended scale environments in real-time. This is achieved through (i) altering the original algorithm such that the region of space being mapped by the KinectFusion algorithm can vary dynamically, (ii) extracting a dense point cloud from the regions that leave the KinectFusion volume due to this variation, and, (iii) incrementally adding the resulting points to a triangular mesh representation of the environment. The system is implemented as a set of hierarchical multi-threaded components which are capable of operating in real-time. The architecture facilitates the creation and integration of new modules with minimal impact on the performance on the dense volume tracking and surface reconstruction modules. We provide experimental results demonstrating the system’s ability to map areas considerably beyond the scale of the original KinectFusion algorithm including a two story apartment and an extended sequence taken from a car at night. In order to overcome failure of the iterative closest point (ICP) based odometry in areas of low geometric features we have evaluated the Fast Odometry from Vision (FOVIS) system as an alternative. We provide a comparison between the two approaches where we show a trade off between the reduced drift of the visual odometry approach and the higher local mesh quality of the ICP-based approach. Finally we present ongoing work on incorporating full simultaneous localisation and mapping (SLAM) pose-graph optimisation. This paper presents a robust and efficient semidense visual odometry solution for RGB-D cameras. The core of our method is a 2D-3D ICP pipeline which estimates the pose of the sensor by registering the projection of a 3D semidense map of a reference frame with the 2D semi-dense region extracted in the current frame. The processing is speeded up by efficiently implemented approximate nearest neighbour fields under the Euclidean distance criterion, which permits the use of compact Gauss-Newton updates in the optimization. The registration is formulated as a maximum a posterior problem to deal with outliers and sensor noise, and the equivalent weighted least squares problem is consequently solved by iteratively reweighted least squares method. A variety of robust weight functions are tested and the optimum is determined based on the probabilistic characteristics of the sensor model. Extensive evaluation on publicly available RGB-D datasets shows that the proposed method predominantly outperforms existing state-of-the-art methods. RGB-D cameras provide both color images and per-pixel depth estimates. The richness of this data and the recent development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a system for visual odometry and mapping using an RGB-D camera, and its application to autonomous flight. By leveraging results from recent state-of-the-art algorithms and hardware, our system enables 3D flight in cluttered environments using only onboard sensor data. All computation and sensing required for local position control are performed onboard the vehicle, reducing the dependence on an unreliable wireless link to a ground station. However, even with accurate 3D sensing and position estimation, some parts of the environment have more perceptual structure than others, leading to state estimates that vary in accuracy across the environment. If the vehicle plans a path without regard to how well it can localize itself along that path, it runs the risk of becoming lost or worse. We show how the belief roadmap algorithm prentice2009belief, a belief space extension of the probabilistic roadmap algorithm, can be used to plan vehicle trajectories that incorporate the sensing model of the RGB-D camera. We evaluate the effectiveness of our system for controlling a quadrotor micro air vehicle, demonstrate its use for constructing detailed 3D maps of an indoor environment, and discuss its limitations. The goal of our work is to complete the depth channel of an RGB-D image. Commodity-grade depth cameras often fail to sense depth for shiny, bright, transparent, and distant surfaces. To address this problem, we train a deep network that takes an RGB image as input and predicts dense surface normals and occlusion boundaries. Those predictions are then combined with raw depth observations provided by the RGB-D camera to solve for depths for all pixels, including those missing in the original observation. This method was chosen over others (e.g., inpainting depths directly) as the result of extensive experiments with a new depth completion benchmark dataset, where holes are filled in training data through the rendering of surface reconstructions created from multiview RGB-D scans. Experiments with different network inputs, depth representations, loss functions, optimization methods, inpainting methods, and deep depth estimation networks show that our proposed approach provides better depth completions than these alternatives. The goal of our work is to complete the depth channel of an RGB-D image. Commodity-grade depth cameras often fail to sense depth for shiny, bright, transparent, and distant surfaces. To address this problem, we train a deep network that takes an RGB image as input and predicts dense surface normals and occlusion boundaries. Those predictions are then combined with raw depth observations provided by the RGB-D camera to solve for depths for all pixels, including those missing in the original observation. This method was chosen over others (e.g., inpainting depths directly) as the result of extensive experiments with a new depth completion benchmark dataset, where holes are filled in training data through the rendering of surface reconstructions created from multiview RGB-D scans. Experiments with different network inputs, depth representations, loss functions, optimization methods, inpainting methods, and deep depth estimation networks show that our proposed approach provides better depth completions than these alternatives. Modern consumer RGB-D cameras are affordable and provide dense depth estimates at high frame rates. Hence, they are popular for building dense environment representations. Yet, the sensors often do not provide accurate depth estimates since the factory calibration exhibits a static deformation. We present a novel approach to online depth calibration that uses a visual SLAM system as reference for the measured depth. A sparse map is generated and the visual information is used to correct the static deformation of the measured depth while missing data is extrapolated using a small number of thin plate splines (TPS). The corrected depth can then be used to improve the accuracy of the sparse RGB-D map and the 3D environment reconstruction. As more data becomes available, the depth calibration is updated on the fly. Our method does not rely on a planar geometry like walls or a one-to-one-pixel correspondence between color and depth camera. Our approach is evaluated in real-world scenarios and against ground truth data. Comparison against two popular self-calibration methods is performed. Furthermore, we show clear visual improvement on aggregated point clouds with our method.