Author name cluster

Supreeth Achar

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8 papers

2 author rows

ICRA Conference 2011 Conference Paper

Large scale visual localization in urban environments

Supreeth Achar
C. V. Jawahar
K. Madhava Krishna

This paper introduces a vision based localization method for large scale urban environments. The method is based upon Bag-of-Words image retrieval techniques and handles problems that arise in urban environments due to repetitive scene structure and the presence of dynamic objects like vehicles. The localization system was experimentally verified it localization experiments along a 5km long path in an urban environment.

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IROS Conference 2011 Conference Paper

Perception for a river mapping robot

Andrew Chambers
Supreeth Achar
Stephen T. Nuske
Joern Rehder
Bernd Kitt
Lyle Chamberlain
Justin Haines
Sebastian A. Scherer

We consider the perceptual challenges inherent in the robotic manipulation of previously unseen socks, with the end goal of manipulation by a household robot for laundry. The task poses challenging problems in modeling the appearance, shape and configuration of these textile items that tend to exhibit high variability in texture, design, and style while being highly articulated objects.

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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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IROS Conference 2011 Conference Paper

Yield estimation in vineyards by visual grape detection

Stephen T. Nuske
Supreeth Achar
Terry Bates
Srinivasa G. Narasimhan
Sanjiv Singh

The harvest yield in vineyards can vary significantly from year to year and also spatially within plots due to variations in climate, soil conditions and pests. Fine grained knowledge of crop yields can allow viticulturists to better manage their vineyards. The current industry practice for yield prediction is destructive, expensive and spatially sparse - during the growing season sparse samples are taken and extrapolated to determine overall yield. We present an automated method that uses computer vision to detect and count grape berries. The method could potentially be deployed across large vineyards taking measurements at every vine in a non-destructive manner. Our berry detection uses both shape and visual texture and we can demonstrate detection of green berries against a green leaf background. Berry detections are counted and the eventual harvest yield is predicted. Results are presented for 224 vines (over 450 meters) of two different grape varieties and compared against the actual harvest yield as groundtruth. We calibrate our berry count to yield and find that we can predict yield of individual vineyard rows to within 9. 8% of actual crop weight.

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AAMAS Conference 2010 Conference Paper

Image Based Exploration for Indoor Environments using Local Features

Aravindhan Krishnan
Madhava Krishna
Supreeth Achar

This paper presents an approach to explore an unknownindoor environment using vision as the sensing modality, thereby building a topological map of images. The contribution of this paper is in a new approach that identifiesthe next best place to move from a node in the topologicalgraph. This decision is taken locally at a node by choosingthe next best direction, when there are open spaces beforethe robot, and globally by choosing the next best node tobranch off a new exploration, when there are no open spacesbefore the robot. We propose a method to assign weightsto nodes for this purpose. Weight is defined as a function ofthe depth of local descriptors of images, and the number oftimes they were seen across different nodes. The efficacy ofthe approach to explore office like environments is verifiedthrough several experiments on a P3DX robot.

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ICRA Conference 2008 Conference Paper

Autonomous image-based exploration for mobile robot navigation

D. Santosh 0001
Supreeth Achar
C. V. Jawahar

Image-based navigation paradigms have recently emerged as an interesting alternative to conventional modelbased methods in mobile robotics. In this paper, we augment the existing image-based navigation approaches by presenting a novel image-based exploration algorithm. The algorithm facilitates a mobile robot equipped only with a monocular pan-tilt camera to autonomously explore a typical indoor environment. The algorithm infers frontier information directly from the images and displaces the robot towards regions that are informative for navigation. The frontiers are detected using a geometric context-based segmentation scheme that exploits the natural scene structure in indoor environments. In the due process, a topological graph of the workspace is built in terms of images which can be subsequently utilised for the tasks of localisation, path planning and navigation. Experimental results on a mobile robot in an unmodified laboratory and corridor environments demonstrate the validity of the approach.

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AAMAS Conference 2008 Conference Paper

Coordination in Ambiguity: Coordinated Active Localization for Multiple Robots

Shivudu Bhuvanagiri
Madhava Krishna
Supreeth Achar

In environments which possess relatively few features that enable a robot to unambiguously determine its location, global localization algorithms can result in multiple hypotheses locations for a robot which makes active guidance for localization necessary. When extended to multi robotic scenarios where all robots possess more than one hypothesis of their position, there is the opportunity to do better by using robots apart from obstacles as ‘hypotheses resolving agents’. The demo here showcases a unified framework accounting for the map structure as well as measurement amongst robots while guiding a set of robots to positions where they can localize to a unique state. Another aspect of framework demonstrates the idea of dispatching localized robots to locations where they can assist a maximum of the remaining unlocalized robots to overcome their ambiguity. The method presented has been tested in both simulation and real-time on robots and its efficacy verified.

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ICRA Conference 2008 Conference Paper

Visual servoing based on Gaussian mixture models

A. H. Abdul Hafez
Supreeth Achar
C. V. Jawahar

In this paper we present a novel approach to robust visual servoing. This method removes the feature tracking step from a typical visual servoing algorithm. We do not need correspondences of the features for deriving the control signal. This is achieved by modeling the image features as a Mixture of Gaussians in the current as well as desired images. Using Lyapunov theory, a control signal is derived to minimize a distance function between the two Gaussian mixtures. The distance function is given in a closed form, and its gradient can be efficiently computed and used to control the system. For simplicity, we first consider the 2D motion case. Then, the general case is presented by introducing the depth distribution of the features to control the six degrees of freedom. Experiments are conducted within a simulation framework to validate our proposed method.

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