PoCo: Point Context Cluster for RGBD Indoor Place Recognition

We present a novel end-to-end algorithm (PoCo) for the indoor RGB-D place recognition task, aimed at identifying the most likely match for a given query frame within a reference database. The task presents inherent challenges attributed to the constrained field of view and limited range of perception sensors. We propose a new network architecture, which generalizes the recent Context of Clusters (CoCs) to extract global descriptors directly from the noisy point clouds through end-to-end learning. Moreover, we develop the architecture by integrating both color and geometric modalities into the point features to enhance the global descriptor representation. We conducted evaluations on public datasets ScanNet-PR and ARKit with 807 and 5047 scenarios, respectively. PoCo achieves SOTA performance: on ScanNet-PR, we achieve R@1 of 64.63%, a 5.7% improvement from the best-published result CGis (61.12%); on Arkit, we achieve R@1 of 45.12%, a 13.3% improvement from the best-published result CGis (39.82%). In addition, PoCo shows higher efficiency than CGis in inference time (1.75X-faster), and we demonstrate the effectiveness of PoCo in recognizing places within a real-world laboratory environment.

Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior

Reconstructing transparent objects using affordable RGB-D cameras is a persistent challenge in robotic perception due to inconsistent appearances across views in the RGB domain and inaccurate depth readings in each single-view. We introduce a two-stage pipeline for reconstructing transparent objects tailored for mobile platforms. In the first stage, off-the-shelf monocular object segmentation and depth completion networks are leveraged to predict the depth of transparent objects, furnishing single-view shape prior. Subsequently, we propose Epipolar-guided Optical Flow (EOF) to fuse several single-view shape priors from the first stage to a cross-view consistent 3D reconstruction given camera poses estimated from opaque part of the scene. Our key innovation lies in EOF which employs boundary-sensitive sampling and epipolar-line constraints into optical flow to accurately establish 2D correspondences across multiple views on transparent objects. Quantitative evaluations demonstrate that our pipeline significantly outperforms baseline methods in 3D reconstruction quality, paving the way for more adept robotic perception and interaction with transparent objects.

LIT: Light-field Inference of Transparency for Refractive Object Localization

Translucency is prevalent in everyday scenes. As such, perception of transparent objects is essential for robots to perform manipulation. Compared with texture-rich or texture-less Lambertian objects, transparency induces significant uncertainty on object appearance. Ambiguity can be due to changes in lighting, viewpoint, and backgrounds, each of which brings challenges to existing object pose estimation algorithms. In this work, we propose LIT, a two-stage method for transparent object pose estimation using light-field sensing and photorealistic rendering. LIT employs multiple filters specific to light-field imagery in deep networks to capture transparent material properties combined with robust depth and pose estimators based on generative sampling. Along with the LIT algorithm, we introduce the first light-field transparent object dataset for the task of recognition, localization and pose estimation. Using proposed algorithm on our dataset, we show that LIT outperforms both a state-of-the-art end-to-end pose estimation method and a generative pose estimator on transparent objects.

LiTE: Light-field Transparency Estimation for Refractive Object Localization

Translucency is prevalent in everyday scenes. As such, perception of transparent objects is essential for robots to perform manipulation. Compared with texture-rich or texture-less Lambertian objects, transparency induces significant uncertainty on object appearance. Ambiguity can be due to changes in lighting, viewpoint, and backgrounds, each of which brings challenges to existing object pose estimation algorithms. In this work, we propose LiTE, a two-stage method for transparent object pose estimation using light-field sensing and photorealistic rendering. LiTE employs multiple filters specific to light-field imagery in deep networks to capture transparent material properties combined with robust depth and pose estimators based on generative sampling. Along with the LiTE algorithm, we introduce the first light-field transparent object dataset for the task of recognition, localization and pose estimation. Using proposed algorithm on our dataset, we show that LiTE outperforms both a state-of-the-art end-to-end pose estimation method and a generative pose estimator on transparent objects.

GlassLoc: Plenoptic Grasp Pose Detection in Transparent Clutter

Transparent objects are prevalent across many environments of interest for dexterous robotic manipulation. Such transparent material leads to considerable uncertainty for robot perception and manipulation, and remains an open challenge for robotics. This problem is exacerbated when multiple transparent objects cluster into piles of clutter. In household environments, for example, it is common to encounter piles of glassware in kitchens, dining rooms, and reception areas, which are essentially invisible to modern robots. We present the GlassLoc algorithm for grasp pose detection of transparent objects in transparent clutter using plenoptic sensing. GlassLoc classifies graspable locations in space informed by a Depth Likelihood Volume (DLV) descriptor. We extend the DLV to infer the occupancy of transparent objects over a given space from multiple plenoptic viewpoints. We demonstrate and evaluate the GlassLoc algorithm on a Michigan Progress Fetch mounted with a first-generation Lytro. The effectiveness of our algorithm is evaluated through experiments for grasp detection and execution with a variety of transparent glassware in minor clutter.

Plenoptic Monte Carlo Object Localization for Robot Grasping under Layered Translucency

In order to fully function in human environments, robot perception will need to account for the uncertainty caused by translucent materials. Translucency poses several open challenges in the form of transparent objects (e.g., drinking glasses), refractive media (e.g., water), and diffuse partial occlusions (e.g., objects behind stained glass panels). This paper presents Plenoptic Monte Carlo Localization (PMCL) as a method for localizing object poses in the presence of translucency using plenoptic (light-field) observations. We propose a new depth descriptor, the Depth Likelihood Volume (DLV), and its use within a Monte Carlo object localization algorithm. We present results of localizing and manipulating objects with translucent materials and objects occluded by layers of translucency. Our PMCL implementation uses observations from a Lytro first generation light field camera to allow a Michigan Progress Fetch robot to perform grasping.

Semantic Robot Programming for Goal-Directed Manipulation in Cluttered Scenes

We present the Semantic Robot Programming (SRP) paradigm as a convergence of robot programming by demonstration and semantic mapping. In SRP, a user can directly program a robot manipulator by demonstrating a snapshot of their intended goal scene in workspace. The robot then parses this goal as a scene graph comprised of object poses and inter-object relations, assuming known object geometries. Task and motion planning is then used to realize the user's goal from an arbitrary initial scene configuration. Even when faced with different initial scene configurations, SRP enables the robot to seamlessly adapt to reach the user's demonstrated goal. For scene perception, we propose the Discriminatively-Informed Generative Estimation of Scenes and Transforms (DIGEST) method to infer the initial and goal states of the world from RGBD images. The efficacy of SRP with DIGEST perception is demonstrated for the task of tray-setting with a Michigan Progress Fetch robot. Scene perception and task execution are evaluated with a public household occlusion dataset and our cluttered scene dataset.

SUM: Sequential Scene Understanding and Manipulation

In order to perform autonomous sequential manipulation tasks, perception in cluttered scenes remains a critical challenge for robots. In this paper, we propose a probabilistic approach for robust sequential scene estimation and manipulation - Sequential Scene Understanding and Manipulation(SUM). SUM considers uncertainty due to discriminative object detection and recognition in the generative estimation of the most likely object poses maintained over time to achieve a robust estimation of the scene under heavy occlusions and unstructured environment. Our method utilizes candidates from discriminative object detector and recognizer to guide the generative process of sampling scene hypothesis, and each scene hypotheses is evaluated against the observations. Also SUM maintains beliefs of scene hypothesis over robot physical actions for better estimation and against noisy detections. We conduct extensive experiments to show that our approach is able to perform robust estimation and manipulation.