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Sourish Ghosh

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3 papers

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ICRA Conference 2023 Conference Paper

AirTrack: Onboard Deep Learning Framework for Long-Range Aircraft Detection and Tracking

Sourish Ghosh
Jay Patrikar
Brady G. Moon
Milad Moghassem Hamidi
Sebastian A. Scherer

Detect-and-Avoid (DAA) capabilities are critical for safe operations of unmanned aircraft systems (UAS). This paper introduces, AirTrack, a real-time vision-only detect and tracking framework that respects the size, weight, and power (SWaP) constraints of sUAS systems. Given the low Signal-to-Noise ratios (SNR) of far away aircraft, we propose using full resolution images in a deep learning framework that aligns successive images to remove ego-motion. The aligned images are then used downstream in cascaded primary and secondary classifiers to improve detection and tracking performance on multiple metrics. We show that AirTrack outperforms state-of-the art baselines on the Amazon Airborne Object Tracking (AOT) Dataset. Multiple real world flight tests with a Cessna 182 interacting with general aviation traffic and additional near-collision flight tests with a Bell helicopter flying towards a UAS in a controlled setting showcase that the proposed approach satisfies the newly introduced ASTM F3442/F3442M standard for DAA. Empirical evaluations show that our system has a probability of track of more than 95% up to a range of 700m. [Video] 1 1 Video: https://youtu.be/bMw5nUGL5GQ

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IROS Conference 2018 Conference Paper

Probabilistic Kinematic State Estimation for Motion Planning of Planetary Rovers

Sourish Ghosh
Kyohei Otsu
Masahiro Ono

Kinematics-based collision detection is important for robot motion planning in unstructured terrain. Especially, planetary rovers require such capability as a single collision may lead to the termination of a mission. For onboard computation, typical numeric approaches are unsuitable as they are computationally expensive and unstable on rocky terrain; instead, a light-weight analytic solution (ACE: Approximate Clearance Evaluation) is planning to be used for the Mars 2020 rover mission. ACE computes the state bounds of articulated suspension systems from terrain height bounds, and assess the safety by checking the constraint violation of states with the worst-case values. ACE's conservative safety check approach can sometimes lead to over-pessimism: feasible states are often reported as infeasible, thus resulting in frequent false positive detection. In this paper, we introduce a computationally efficient probabilistic variant of ACE (called p-ACE) which estimates the probability distributions of states in real time. The advantage of having probability distributions over states, instead of deterministic bounds, is to provide more flexible and less pessimistic worst-case evaluation with probabilistic safety guarantees. Empirically derived distribution models are used to compute the total probability of constraint satisfaction, which is then used for path assessment. Through experiments with a high-fidelity simulator, we empirically show that p-ACE relaxes the deterministic state bounds without losing safety guarantees.

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IROS Conference 2017 Conference Paper

Joint perception and planning for efficient obstacle avoidance using stereo vision

Sourish Ghosh
Joydeep Biswas

Stereo vision is commonly used for local obstacle avoidance of autonomous mobile robots: stereo images are first processed to yield a dense 3D reconstruction of the observed scene, which is then used for navigation planning. Such an approach, which we term Sequential Perception and Planning (SPP), results in significant unnecessary computations as the navigation planner only needs to explore a small part of the scene to compute the shortest obstacle-free path. In this paper, we introduce an approach to Joint Perception and Planning (JPP) using stereo vision, which performs disparity checks on demand, only as necessary while searching on a planning graph. Furthermore, obstacle checks for navigation planning do not require full 3D reconstruction: we present in this paper how obstacle queries can be decomposed into a sequence of confident positive stereo matches and confident negative stereo matches, which are significantly faster to compute than the exact depth of points. The resulting complete JPP formulation is significantly faster than SPP, while still maintaining correctness of planning. We also show how the JPP works with different planners, including search-based and sampling-based planners. We present extensive experimental results from real robot data and simulation experiments, demonstrating that the JPP requires less than 10% of the disparity computations required by SPP.

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