Pushyami Kaveti

Emmett Wise, Pushyami Kaveti, Qilong Chen et al.

Automatic extrinsic sensor calibration is a fundamental problem for multi-sensor platforms. Reliable and general-purpose solutions should be computationally efficient, require few assumptions about the structure of the sensing environment, and demand little effort from human operators. In this work, we introduce a fast and certifiably globally optimal algorithm for solving a generalized formulation of the robot-world and hand-eye calibration (RWHEC) problem. The formulation of RWHEC presented is "generalized" in that it supports the simultaneous estimation of multiple sensor and target poses, and permits the use of monocular cameras that, alone, are unable to measure the scale of their environments. In addition to demonstrating our method's superior performance over existing solutions through extensive simulated and real experiments, we derive novel identifiability criteria and establish a priori guarantees of global optimality for problem instances with bounded measurement errors. As part of our analysis, we propose a new constraint qualification for nonlinear programs with redundant constraints; this constraint qualification is of independent interest for establishing the exactness of SDP relaxations of QCQPs that have been tightened through the addition of redundant constraints. Finally, we provide a free and open-source implementation of our algorithms and experiments.

Zhiyong Zhang, Pushyami Kaveti, Hanumant Singh et al.

In this paper, we present a methodology for fisheries-related data that allows us to converge on a labeled image dataset by iterating over the dataset with multiple training and production loops that can exploit crowdsourcing interfaces. We present our algorithm and its results on two separate sets of image data collected using the Seabed autonomous underwater vehicle. The first dataset comprises of 2,026 completely unlabeled images, while the second consists of 21,968 images that were point annotated by experts. Our results indicate that training with a small subset and iterating on that to build a larger set of labeled data allows us to converge to a fully annotated dataset with a small number of iterations. Even in the case of a dataset labeled by experts, a single iteration of the methodology improves the labels by discovering additional complicated examples of labels associated with fish that overlap, are very small, or obscured by the contrast limitations associated with underwater imagery.

Colin Keil, Aniket Gupta, Pushyami Kaveti et al.

Visual SLAM with thermal imagery, and other low contrast visually degraded environments such as underwater, or in areas dominated by snow and ice, remain a difficult problem for many state of the art (SOTA) algorithms. In addition to challenging front-end data association, thermal imagery presents an additional difficulty for long term relocalization and map reuse. The relative temperatures of objects in thermal imagery change dramatically from day to night. Feature descriptors typically used for relocalization in SLAM are unable to maintain consistency over these diurnal changes. We show that learned feature descriptors can be used within existing Bag of Word based localization schemes to dramatically improve place recognition across large temporal gaps in thermal imagery. In order to demonstrate the effectiveness of our trained vocabulary, we have developed a baseline SLAM system, integrating learned features and matching into a classical SLAM algorithm. Our system demonstrates good local tracking on challenging thermal imagery, and relocalization that overcomes dramatic day to night thermal appearance changes. Our code and datasets are available here: https://github.com/neufieldrobotics/IRSLAM_Baseline

Pushyami Kaveti, Hanumant Singh

In this chapter we discuss the problem of master failure in ROS1.0 and its impact on robotic deployments in the real world. We address this issue in this tutorial chapter where we outline, design and demonstrate a fault tolerant mechanism associated with ROS master failure. Unlike previous solutions which use primary backup replication and external checkpointing libraries which are process heavy, our mechanism adds a lightweight functionality to the ROS master to enable it to recover from failure. We present a modified version of ROS master which is equipped with a logging mechanism to record the meta information and network state of ROS nodes as well as a recovery mechanism to go back to the previous state without having to abort or restart all the nodes. We also implement an additional master monitor node responsible for failure detection on the master by polling it for its availability. Our code is implemented in python and preliminary tests were conducted successfully on a variety of land, aerial and underwater robots and a tele-operating computer running ROS Kinetic on Ubuntu 16.04. The code is publicly available under a creative commons license on github at https://github.com/PushyamiKaveti/fault-tolerant-ros-master

Pushyami Kaveti, Hanumant Singh

There is a general expectation that robots should operate in urban environments often consisting of potentially dynamic entities including people, furniture and automobiles. Dynamic objects pose challenges to visual SLAM algorithms by introducing errors into the front-end. This paper presents a Light Field SLAM front-end which is robust to dynamic environments. A Light Field captures a bundle of light rays emerging from a single point in space, allowing us to see through dynamic objects occluding the static background via Synthetic Aperture Imaging(SAI). We detect apriori dynamic objects using semantic segmentation and perform semantic guided SAI on the Light Field acquired from a linear camera array. We simultaneously estimate both the depth map and the refocused image of the static background in a single step eliminating the need for static scene initialization. The GPU implementation of the algorithm facilitates running at close to real time speeds of 4 fps. We demonstrate that our method results in improved robustness and accuracy of pose estimation in dynamic environments by comparing it with state of the art SLAM algorithms.

Pushyami Kaveti, Sammie Katt, Hanumant Singh

There is a general expectation that robots should operate in environments that consist of static and dynamic entities including people, furniture and automobiles. These dynamic environments pose challenges to visual simultaneous localization and mapping (SLAM) algorithms by introducing errors into the front-end. Light fields provide one possible method for addressing such problems by capturing a more complete visual information of a scene. In contrast to a single ray from a perspective camera, Light Fields capture a bundle of light rays emerging from a single point in space, allowing us to see through dynamic objects by refocusing past them. In this paper we present a method to synthesize a refocused image of the static background in the presence of dynamic objects that uses a light-field acquired with a linear camera array. We simultaneously estimate both the depth and the refocused image of the static scene using semantic segmentation for detecting dynamic objects in a single time step. This eliminates the need for initializing a static map . The algorithm is parallelizable and is implemented on GPU allowing us execute it at close to real time speeds. We demonstrate the effectiveness of our method on real-world data acquired using a small robot with a five camera array.

Pushyami Kaveti, Md Navid Akbar

Biologists and scientists have been tackling the problem of marine life monitoring and fish stock estimation for many years now. Efforts are now directed to move towards non-intrusive methods, by utilizing specially designed underwater robots to collect images of the marine population. Training machine learning algorithms on the images collected, we can now estimate the population. This in turn helps to impose regulations to control overfishing. To train these models, however, we need annotated images. Annotation of large sets of images collected over a decade is quite challenging. Hence, we resort to Amazon Mechanical Turk (MTurk), a crowdsourcing platform, for the image annotation task. Although it is fast to get work done in MTurk, the work obtained is often of poor quality. This work aims to understand the human factors in designing Human Intelligence Tasks (HITs), from the perspective of the Self-Determination Theory. Applying elements from the theory, we design an HIT to increase the competence and motivation of the workers. Within our experimental framework, we find that the new interface significantly improves the accuracy of worker performance.