Abstract

It is critical for aerial robots flying in city scale urban environments to make very quick estimates of a building depth with respect to itself. It should be done in a matter of few views to navigate itself, avoiding collisions with such a towering structure. As such, no one has attacked this problem. We bring together several modules combining deep learning and 3D vision to showcase a quick reconstruction in a few views. We exploit the inherent planar structure in the buildings (facades, windows) for this purpose. We evaluate the efficacy of our pipeline with various constraints and errors from multi-view geometry using ablation studies. We then retrieve the skyline of the buildings in synthetic as well as real-world scenes.

Abstract

Surface normal estimation is an essential component of several computer and robot vision pipelines. While this problem has been extensively studied, most approaches are geared towards indoor scenes and often rely on multiple modalities (depth, multiple views) for accurate estimation of normal maps. Outdoor scenes pose a greater challenge as they exhibit significant lighting variation, often contain occluders, and structures like building facades are often ridden with numerous windows and protrusions. Conventional supervised learning schemes excel in indoor scenes, but do not exhibit competitive performance when trained and deployed in outdoor environments. Furthermore, they involve complex network architectures and require many more trainable parameters. To tackle these challenges, we present an adversarial learning scheme that regularizes the output normal maps from a neural network to appear more realistic, by using a small number of precisely annotated examples. Our method presents a lightweight and simpler architecture, while improving performance by at least 1.5x across most metrics. We evaluate our approaches against the state-of-the-art on normal map estimation, on a synthetic and a real outdoor dataset, and observe significant performance enhancements.

Abstract

Given a monocular color image of a warehouse rack, we aim to predict the bird's-eye view layout for each shelf in the rack, which we term as' multi-layer'layout prediction. To this end, we present RackLay, a deep neural network for real-time shelf layout estimation from a single image. Unlike previous layout estimation methods which provide a single layout for the dominant ground plane alone, RackLay estimates the top-viewand front-view layout for each shelf in the considered rack populated with objects. RackLay's architecture and its variants are versatile and estimate accurate layouts for diverse scenes characterized by varying number of visible shelves in an image, large range in shelf occupancy factor and varied background clutter. Given the extreme paucity of datasets in this space and the difficulty involved in acquiring real data from warehouses, we additionally release a flexible synthetic dataset generation …

Abstract

Human-robot interaction, particularly in wheeled mobile robots that can autonomously assist humans to traverse dynamically changing environments is a field of active research. Integrated motion planning and obstacle-avoidance pose a considerable challenge for an autonomous person-following robot (PFR). And, scenarios with intersections and occlusions along the path only increase the complexity in sustained tracking. In this paper, we use model predictive control (MPC) with earlyrelocation (ER) strategy to formulate a prioritized tracking scheme and implement it for a differential-drive system. Our approach ensures that the target person stays within the field of view (FOV) of the PFR consistently, even while it maneuvers intersections or crowded spots, by adding new locations to its updated path. As trajectory generation in such cases must be incremental to accommodate new information, the use of efficient representations is key. To that end, we build this social representation of following a person directly into the controller itself. MPC can naturally handle such state and input limitations as constraints to solve an on-line optimization at each time step. A non-linear MPC with ER is thus devised and tested with increasing levels of complexity arising from occlusions due to the map and its dynamic actors. By using 2D simulations, we show that for slow and medium walking speeds of the target person, the controller can plan maneuvers with an adequate margin of over 20 Hz apt for achieving a near real-time personfollowing behaviour.

Abstract

This paper presents the design of an in-pipe climbing robot that operates using a novel ‘Three-output open differential’(3-OOD) mechanism to traverse complex networks of pipes. Conventional wheeled/tracked in-pipe climbing robots are prone to slip and drag while traversing in pipe bends. The 3-OOD mechanism helps in achieving the novel result of eliminating slip and drag in the robot tracks during motion. The proposed differential realizes the functional abilities of the traditional two-output differential, which is achieved the first time for a differential with three outputs. The 3-OOD mechanism mechanically modulates the track speeds of the robot based on the forces exerted on each track inside the pipe network, by eliminating the need for any active control. The simulation of the robot traversing in the pipe network in different orientations and in pipe-bends without slip shows the proposed design’s effectiveness.

Abstract

This paper proposes a novel method for reactive multiagent collision avoidance by characterizing the longitudinal and lateral intent uncertainty along a trajectory as a closed-form probability density function. Intent uncertainty is considered as the set of reachable velocities in a planning interval and distributed as a Gaussian distribution over the robot’s instantaneous velocity. We utilize the Time Scaled Collision Cone(TSCC) approach, which characterizes the space of instantaneous collision avoidance velocities available to the egoagent. We introduce intent uncertainty into the characteristic equation of the TSCC to derive the closed-form probability density function, which allows the collision avoidance problem to be rewritten as a deterministic optimization procedure. The formulation also allows the flexibility for the inclusion of confidence intervals for collision avoidance. We thus demonstrate the results and ablation studies of this derived collision avoidance formulation on various confidence intervals

Abstract

We present CCO-VOXEL: the very first chance-constrained optimization (CCO) algorithm that can compute trajectory plans with probabilistic safety guarantees in real-time directly on the voxel-grid representation of the world. CCO-VOXEL maps the distribution over the distance to the closest obstacle to a distribution over collision-constraint violation and computes an optimal trajectory that minimizes the violation probability. Importantly, unlike existing works, we never assume the nature of the sensor uncertainty or the probability distribution of the resulting collision-constraint violations. We leverage the notion of Hilbert Space embedding of distributions and Maximum Mean Discrepancy (MMD) to compute a tractable surrogate for the original chance-constrained optimization problem and employ a combination of A* based graph-search and Cross-Entropy Method for obtaining its minimum. We show tangible performance gain in terms of collision avoidance and trajectory smoothness as a consequence of our probabilistic formulation vis a vis state-of-the-art planning methods that do not account for such nonparametric noise. Finally, we also show how a combination of low-dimensional feature embedding and pre-caching of Kernel Matrices of MMD allows us to achieve real-time performance in simulations as well as in implementations on on-board commodity hardware that controls the quadrotor flight

Abstract

We embark on a hitherto unreported problem of an autonomous robot (self-driving car) navigating in dynamic scenes in a manner that reduces its localization error and eventual cumulative drift or Absolute Trajectory Error, which is pronounced in such dynamic scenes. With the hugely popular Velodyne-16 3D LIDAR as the main sensing modality, and the accurate LIDAR-based Localization and Mapping algorithm, LOAM, as the state estimation framework, we show that in the absence of a navigation policy, drift rapidly accumulates in the presence of moving objects. To overcome this, we learn actions that lead to drift-minimized navigation through a suitable set of reward and penalty functions. We use Proximal Policy Optimization, a class of Deep Reinforcement Learning methods, to learn the actions that result in drift-minimized trajectories. We show by extensive comparisons on a variety of synthetic, yet photo-realistic scenes made available through the CARLA Simulator the superior performance of the proposed framework vis-a-vis methods that do not adopt such policies.

Abstract

We present DRACO, a method for Dense Reconstruction And Canonicalization of Object shape from one or more RGB images. Canonical shape reconstruction—estimating 3D object shape in a coordinate space canonicalized for scale, rotation, and translation parameters—is an emerging paradigm that holds promise for a multitude of robotic applications. Prior approaches either rely on painstakingly gathered dense 3D supervision, or produce only sparse canonical representations, limiting real-world applicability. DRACO performs dense canonicalization using only weak supervision in the form of camera poses and semantic keypoints at train time. During inference, DRACO predicts dense object-centric depth maps in a canonical coordinate-space, solely using one or more RGB images of an object. Extensive experiments on canonical shape reconstruction and pose estimation show that DRACO is competitive or superior to fully-supervised methods.

Abstract

Modern visual-inertial navigation systems (VINS) are faced with a critical challenge in real-world deployment: they need to operate reliably and robustly in highly dynamic environments. Current best solutions merely filter dynamic objects as outliers based on the semantics of the object category. Such an approach does not scale as it requires semantic classifiers to encompass all possibly-moving object classes; this is hard to define, let alone deploy. On the other hand, many realworld environments exhibit strong structural regularities in the form of planes such as walls and ground surfaces, which are also crucially static. We present RP-VIO, a monocular visual-inertial odometry system that leverages the simple geometry of these planes for improved robustness and accuracy in challenging dynamic environments. Since existing datasets have a limited number of dynamic elements, we also present a highly-dynamic, photorealistic synthetic dataset for a more effective evaluation of the capabilities of modern VINS systems. We evaluate our approach on this dataset, and three diverse sequences from standard datasets including two real-world dynamic sequences and show a significant improvement in robustness and accuracy over a state-of-the-art monocular visual-inertial odometry system. We also show in simulation an improvement over a simple dynamic-features masking approach. Our code and dataset are publicly available .