Dense Piecewise Planar RGB-D SLAM for Indoor Environments

The paper exploits weak Manhattan constraints to parse the structure of indoor environments from RGB-D video sequences in an online setting. We extend the previous approach for single view parsing of indoor scenes to video sequences and formulate the problem of recovering the floor plan of the environment as an optimal labeling problem solved using dynamic programming. The temporal continuity is enforced in a recursive setting, where labeling from previous frames is used as a prior term in the objective function. In addition to recovery of piecewise planar weak Manhattan structure of the extended environment, the orthogonality constraints are also exploited by visual odometry and pose graph optimization. This yields reliable estimates in the presence of large motions and absence of distinctive features to track. We evaluate our method on several challenging indoors sequences demonstrating accurate SLAM and dense mapping of low texture environments. On existing TUM benchmark we achieve competitive results with the alternative approaches which fail in our environments.Comment: International Conference on Intelligent Robots and Systems (IROS) 201

Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments

Existing simultaneous localization and mapping (SLAM) algorithms are not robust in challenging low-texture environments because there are only few salient features. The resulting sparse or semi-dense map also conveys little information for motion planning. Though some work utilize plane or scene layout for dense map regularization, they require decent state estimation from other sources. In this paper, we propose real-time monocular plane SLAM to demonstrate that scene understanding could improve both state estimation and dense mapping especially in low-texture environments. The plane measurements come from a pop-up 3D plane model applied to each single image. We also combine planes with point based SLAM to improve robustness. On a public TUM dataset, our algorithm generates a dense semantic 3D model with pixel depth error of 6.2 cm while existing SLAM algorithms fail. On a 60 m long dataset with loops, our method creates a much better 3D model with state estimation error of 0.67%.Comment: International Conference on Intelligent Robots and Systems (IROS) 201

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

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. 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

Neural Radiance Fields for Manhattan Scenes with Unknown Manhattan Frame

Novel view synthesis and 3D modeling using implicit neural field representation are shown to be very effective for calibrated multi-view cameras. Such representations are known to benefit from additional geometric and semantic supervision. Most existing methods that exploit additional supervision require dense pixel-wise labels or localized scene priors. These methods cannot benefit from high-level vague scene priors provided in terms of scenes' descriptions. In this work, we aim to leverage the geometric prior of Manhattan scenes to improve the implicit neural radiance field representations. More precisely, we assume that only the knowledge of the indoor scene (under investigation) being Manhattan is known -- with no additional information whatsoever -- with an unknown Manhattan coordinate frame. Such high-level prior is used to self-supervise the surface normals derived explicitly in the implicit neural fields. Our modeling allows us to group the derived normals and exploit their orthogonality constraints for self-supervision. Our exhaustive experiments on datasets of diverse indoor scenes demonstrate the significant benefit of the proposed method over the established baselines

Large Scale 3D Mapping of Indoor Environments Using a Handheld RGBD Camera

The goal of this research is to investigate the problem of reconstructing a 3D representation of an environment, of arbitrary size, using a handheld color and depth (RGBD) sensor. The focus of this dissertation is to examine four of the underlying subproblems to this system: camera tracking, loop closure, data storage, and integration. First, a system for 3D reconstruction of large indoor planar environments with data captured from an RGBD sensor mounted on a mobile robotic platform is presented. An algorithm for constructing nearly drift-free 3D occupancy grids of large indoor environments in an online manner is also presented. This approach combines data from an odometry sensor with output from a visual registration algorithm, and it enforces a Manhattan world constraint by utilizing factor graphs to produce an accurate online estimate of the trajectory of the mobile robotic platform. Through several experiments in environments with varying sizes and construction it is shown that this method reduces rotational and translational drift significantly without performing any loop closing techniques. In addition the advantages and limitations of an octree data structure representation of a 3D environment is examined. Second, the problem of sensor tracking, specifically the use of the KinectFusion algorithm to align two subsequent point clouds generated by an RGBD sensor, is studied. A method to overcome a significant limitation of the Iterative Closest Point (ICP) algorithm used in KinectFusion is proposed, namely, its sole reliance upon geometric information. The proposed method uses both geometric and color information in a direct manner that uses all the data in order to accurately estimate camera pose. Data association is performed by computing a warp between the two color images associated with two RGBD point clouds using the Lucas-Kanade algorithm. A subsequent step then estimates the transformation between the point clouds using either a point-to-point or point-to-plane error metric. Scenarios in which each of these metrics fails are described, and a normal covariance test for automatically selecting between them is proposed. Together, Lucas-Kanade data association (LKDA) along with covariance testing enables robust camera tracking through areas of low geometrical features, while at the same time retaining accuracy in environments in which the existing ICP technique succeeds. Experimental results on several publicly available datasets demonstrate the improved performance both qualitatively and quantitatively. Third, the choice of state space in the context of performing loop closure is revisited. Although a relative state space has been discounted by previous authors, it is shown that such a state space is actually extremely powerful, able to achieve recognizable results after just one iteration. The power behind the technique is that changing the orientation of one node is able to affect other nodes. At the same time, the approach --- which is referred to as Pose Optimization using a Relative State Space (POReSS) --- is fast because, like the more popular incremental state space, the Jacobian never needs to be explicitly computed. Furthermore, it is shown that while POReSS is able to quickly compute a solution near the global optimum, it is not precise enough to perform the fine adjustments necessary to achieve acceptable results. As a result, a method to augment POReSS with a fast variant of Gauss-Seidel --- which is referred to as Graph-Seidel --- on a global state space to allow the solution to settle closer to the global minimum is proposed. Through a set of experiments, it is shown that this combination of POReSS and Graph-Seidel is not only faster but achieves a lower residual than other non-linear algebra techniques. Moreover, unlike the linear algebra-based techniques, it is shown that this approach scales to very large graphs. In addition to revisiting the idea of using a relative state space, the benefits of only optimizing the rotational components of a trajectory in order to perform loop closing is examined (rPOReSS). Finally, an incremental implementation of the rotational optimization is proposed (irPOReSS)