Sandeep Konam

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Obstacle avoidance from monocular images is a challenging problem for robots. Though multi-view structurefrom-motion could build 3D maps, it is not robust in textureless environments. Some learning based methods exploit human demonstration to predict a steering command directly from a single image. However, this method is usually biased towards certain(More)
Mango cultivation methods being adopted currently are ineffective and low productive despite consuming huge man power. Advancements in robust unmanned aerial vehicles (UAV's), high speed image processing algorithms and machine vision techniques, reinforce the possibility of transforming agricultural scenario to modernity within prevailing time and energy(More)
In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements,(More)
Automation of object counting in digital images has received significant attention in the last 20 years. Objects under consideration varied from cells, bacteria, trees, fruits, pollen, insects to people. These applications cast light on the importance of shape identification and object counting. We developed an algorithm and methodology for detection of(More)
In this thesis, we focus on making robots more trustable by making them describe and explain their actions. First, to tackle the problem of making robots describe their experience, we introduce the concept of verbalization, a parallel to visualization. Our verbalization algorithm can analyze log files as well as the robot’s live execution data to produce(More)
Our CoBot robots have successfully performed a variety of service tasks in our multi-building environment including accompanying people to meetings and delivering objects to offices due to its navigation and localization capabilities. However, they lack the capability to visually search over desks and other confined locations for an object of interest.(More)
Agribots are soon going to be employed for the labour intensive, menial and tiresome tasks pertaining to agriculture. Cost-effective Agribots are deemed to be the future of agricultural industry for they have been widely portrayed as having the potential to increase agricultural productivity and solve issues daunting farmers. This paper introduces ROTAAI(More)
The predictive power of neural networks often costs model interpretability. Several techniques have been developed for explaining model outputs in terms of input features; however, it is difficult to translate such interpretations into actionable insight. Here, we propose a framework to analyze predictions in terms of the model’s internal features by(More)
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