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3. Instance-Level Semantic Maps for Vision Language Navigation

Author

Nanwani, Laksh, primary, Agarwal, Anmol, additional, Jain, Kanishk, additional, Prabhakar, Raghav, additional, Monis, Aaron, additional, Mathur, Aditya, additional, Jatavallabhula, Krishna Murthy, additional, Abdul Hafez, A. H., additional, Gandhi, Vineet, additional, and Krishna, K. Madhava, additional

Published
2023
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12. Flow Synthesis Based Visual Servoing Frameworks for Monocular Obstacle Avoidance Amidst High-Rises

Author

Sankhla, Harshit K., Qureshi, M. Nomaan, V., Shankara Narayanan, Mittal, Vedansh, Gupta, Gunjan, Pandya, Harit, and Krishna, K. Madhava

Subjects
FOS: Computer and information sciences, Computer Science - Robotics, ComputerApplications_COMPUTERSINOTHERSYSTEMS, Robotics (cs.RO)
Abstract

We propose a novel flow synthesis based visual servoing framework enabling long-range obstacle avoidance for Micro Air Vehicles (MAV) flying amongst tall skyscrapers. Recent deep learning based frameworks use optical flow to do high-precision visual servoing. In this paper, we explore the question: can we design a surrogate flow for these high-precision visual-servoing methods, which leads to obstacle avoidance? We revisit the concept of saliency for identifying high-rise structures in/close to the line of attack amongst other competing skyscrapers and buildings as a collision obstacle. A synthesised flow is used to displace the salient object segmentation mask. This flow is so computed that the visual servoing controller maneuvers the MAV safely around the obstacle. In this approach, we use a multi-step Cross-Entropy Method (CEM) based servo control to achieve flow convergence, resulting in obstacle avoidance. We use this novel pipeline to successfully and persistently maneuver high-rises and reach the goal in simulated and photo-realistic real-world scenes. We conduct extensive experimentation and compare our approach with optical flow and short-range depth-based obstacle avoidance methods to demonstrate the proposed framework's merit. Additional Visualisation can be found at https://sites.google.com/view/monocular-obstacle/home, Accepted to IEEE International Conference on Automation Science and Engineering (CASE), 2022

Published
2022
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16. Grounding Linguistic Commands to Navigable Regions

Author

Rufus, Nivedita, Jain, Kanishk, Nair, Unni Krishnan R, Gandhi, Vineet, and Krishna, K Madhava

Subjects
FOS: Computer and information sciences, Computer Science - Robotics, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Robotics (cs.RO)
Abstract

Humans have a natural ability to effortlessly comprehend linguistic commands such as "park next to the yellow sedan" and instinctively know which region of the road the vehicle should navigate. Extending this ability to autonomous vehicles is the next step towards creating fully autonomous agents that respond and act according to human commands. To this end, we propose the novel task of Referring Navigable Regions (RNR), i.e., grounding regions of interest for navigation based on the linguistic command. RNR is different from Referring Image Segmentation (RIS), which focuses on grounding an object referred to by the natural language expression instead of grounding a navigable region. For example, for a command "park next to the yellow sedan," RIS will aim to segment the referred sedan, and RNR aims to segment the suggested parking region on the road. We introduce a new dataset, Talk2Car-RegSeg, which extends the existing Talk2car dataset with segmentation masks for the regions described by the linguistic commands. A separate test split with concise manoeuvre-oriented commands is provided to assess the practicality of our dataset. We benchmark the proposed dataset using a novel transformer-based architecture. We present extensive ablations and show superior performance over baselines on multiple evaluation metrics. A downstream path planner generating trajectories based on RNR outputs confirms the efficacy of the proposed framework.

Published
2021
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17. RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments

Author

Ram, Karnik, Kharyal, Chaitanya, Harithas, Sudarshan S., and Krishna, K. Madhava

Subjects
FOS: Computer and information sciences, Computer Science - Robotics, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Robotics (cs.RO)
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 real-world 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., Comment: Presented at IROS 2021, code and dataset available at https://karnikram.info/rp-vio

Published
2021
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33. Solving Chance-Constrained Optimization Under Nonparametric Uncertainty Through Hilbert Space Embedding.

Author

Gopalakrishnan, Bharath, Singh, Arun Kumar, Krishna, K. Madhava, and Manocha, Dinesh

Subjects
HILBERT space, CONSTRAINED optimization, KERNEL functions, MOBILE robots, NONLINEAR functions, COMPUTATIONAL complexity
Abstract

In this article, we present an efficient algorithm for solving a class of chance-constrained optimization under nonparametric uncertainty. Our algorithm is built on the possibility of representing arbitrary distributions as functions in Reproducing Kernel Hilbert Space (RKHS). We use this foundation to formulate chance-constrained optimization as one of minimizing the distance between a desired distribution and the distribution of the constraint functions in the RKHS. We provide a systematic way of constructing the desired distribution based on the notion of scenario approximation. Furthermore, we use the kernel trick to show that the computational complexity of our reformulated optimization problem is comparable to solving a deterministic variant of the chance-constrained optimization. We validate our formulation on two important robotic applications: 1) reactive collision avoidance of mobile robots in uncertain dynamic environments and 2) inverse-dynamics-based path-tracking of manipulators under perception uncertainty. In both these applications, the underlying chance constraints are defined over nonlinear and nonconvex functions of uncertain parameters and possibly also decision variables. We also benchmark our formulation with the existing approaches in terms of sample complexity and the achieved optimal cost highlighting significant improvements in both these metrics. [ABSTRACT FROM AUTHOR]

Published
2022
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