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Bharath Sankaran

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5 papers

2 author rows

IROS Conference 2016 Conference Paper

Parameter learning for improving binary descriptor matching

Bharath Sankaran
Srikumar Ramalingam
Yuichi Taguchi

Binary descriptors allow fast detection and matching algorithms in computer vision problems. Though binary descriptors can be computed at almost two orders of magnitude faster than traditional gradient based descriptors, they suffer from poor matching accuracy in challenging conditions. In this paper we propose three improvements for binary descriptors in their computation and matching that enhance their performance in comparison to traditional binary and non-binary descriptors without compromising their speed. This is achieved by learning some weights and threshold parameters that allow customized matching under some variations such as lighting and viewpoint. Our suggested improvements can be easily applied to any binary descriptor. We demonstrate our approach on the ORB (Oriented FAST and Rotated BRIEF) descriptor and compare its performance with the traditional ORB and SIFT descriptors on a wide variety of datasets. In all instances, our enhancements outperform standard ORB and are comparable to SIFT.

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RLDM Conference 2015 Conference Abstract

Policy Learning with Hypothesis based Local Action Selection

Bharath Sankaran
Jeannette Bohg
Nathan Ratliff
Stefan Schaal

For robots to be effective in human environments, they should be capable of successful task execution in unstructured environments. Of these, many task oriented manipulation behaviors executed by robots rely on model based grasping strategies and model based strategies require accurate object detection and pose estimation. Both these tasks are hard in human environments, since human environments are plagued by partial observability and unknown objects. Given these difficulties, it becomes crucial for a robot to be able to operate effectively under partial observability in unrecognized environments. Manipulation in such environments is also particularly hard, since the robot needs to reason about the dynamics of how various objects of unknown or only partially known shape interact with each other under contact. Modelling the dynamic process of a cluttered scene during manipulation is hard even if all object models and poses were known. It becomes even harder to reasonably develop a process or observation model, with only partial information about the object class or shape. To enable a robot to effectively operate in partially observable unknown environments we introduce a policy learning framework where action selection is cast as a probabilistic classification problem on hypothesis sets generated from observations of the environment. The action classifier operates online with a global stopping criterion for successful task completion. The example we consider is object search in clutter, where we assume having access to a visual object detector, that directly populates the hypothesis set given the current observation. Thereby we can avoid the temporal modelling of the process of searching through clutter. We demonstrate our algorithm on two manipulation based object search scenarios; a modified minesweeper simulation and a real world object search in clutter using a dual arm manipulation platform.

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ICRA Conference 2013 Conference Paper

Hypothesis testing framework for active object detection

Nikolay Atanasov 0001
Bharath Sankaran
Jerome Le Ny
Thomas Koletschka
George J. Pappas
Kostas Daniilidis

One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection by controlling the point of view of a mobile depth camera. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. The sensor then plans a sequence of viewpoints, which balances the amount of energy used to move with the chance of identifying the correct hypothesis. We formulate an active M-ary hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate POMDP algorithm. The validity of our approach is verified through simulation and experiments with real scenes captured by a kinect sensor. The results suggest a significant improvement over static object detection.

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IROS Conference 2012 Conference Paper

Failure recovery with shared autonomy

Bharath Sankaran
Benjamin Pitzer
Sarah Osentoski

Building robots capable of long term autonomy has been a long standing goal of robotics research. Such systems must be capable of performing certain tasks with a high degree of robustness and repeatability. In the context of personal robotics, these tasks could range anywhere from retrieving items from a refrigerator, loading a dishwasher, to setting up a dinner table. Given the complexity of tasks there are a multitude of failure scenarios that the robot can encounter, irrespective of whether the environment is static or dynamic. For a robot to be successful in such situations, it would need to know how to recover from failures or when to ask a human for help. This paper, presents a novel shared autonomy behavioral executive to addresses these issues. We demonstrate how this executive combines generalized logic based recovery and human intervention to achieve continuous failure free operation. We tested the systems over 250 trials of two different use case experiments. Our current algorithm drastically reduced human intervention from 26% to 4% on the first experiment and 46% to 9% on the second experiment. This system provides a new dimension to robot autonomy, where robots can exhibit long term failure free operation with minimal human supervision. We also discuss how the system can be generalized.

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ICRA Conference 2011 Conference Paper

Self-supervised segmentation of river scenes

Supreeth Achar
Bharath Sankaran
Stephen T. Nuske
Sebastian A. Scherer
Sanjiv Singh

Here we consider the problem of automatically segmenting images taken from a boat or low-flying aircraft. Such a capability is important for autonomous river following and mapping. The need for accurate segmentation in a wide variety of riverine environments challenges the state of the art vision-based methods that have been used in more structured environments such as roads and highways. Apart from the lack of structure, the principal difficulty is the large spatial and temporal variations in the appearance of water in the presence of nearby vegetation and with reflections from the sky. We propose a self-supervised method to segment images into ‘sky’, ‘river’ and ‘shore’ (vegetation + structures) regions. Our approach uses assumptions about river scene structure to learn appearance models based on features like color, texture and image location which are used to segment the image. We validated our algorithm by testing on four datasets captured under varying conditions on different rivers. Our self-supervised algorithm had higher accuracy rates than a supervised alternative, often significantly more accurate, and does not need to be retrained to work under different conditions.

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