Author name cluster

Srinivasan Seshan

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5 papers

1 author row

ICRA Conference 2016 Conference Paper

Active sensing data collection with autonomous mobile robots

Richard Wang
Manuela Veloso
Srinivasan Seshan

With the introduction of autonomous robots that help perform various tasks in our environments, we can opportunistically use them for collecting fine-grain sensor measurements about our surroundings. Use of mobile robots for data collection scales much better than static sensors in terms of number of measurement locations and provide more fine-grain accuracy and reliability than alternate human crowd-sourcing efforts. One of the unique features of mobile robots is the ability to control and direct where and when measurements should be collected. In this paper, we present a system to compute paths for the robot to follow that incorporates the robot's limited expected deployment time, expected measurement value at each location, and a history of when each location was last visited.

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IROS Conference 2015 Conference Paper

Indoor trajectory identification: Snapping with uncertainty

Richard Wang
Ravi Shroff
Yilong Zha
Srinivasan Seshan
Manuela Veloso

We consider the problem of indoor human trajectory identification using odometry data from smartphone sensors. Given a segmented trajectory, a simplified map of the environment, and a set of error thresholds, we implement a map-matching algorithm in a urban setting and analyze the accuracy of the resulting path. We also discuss aggregation of user step data into a segmented trajectory. Besides providing an interesting application of learning human motion in a constrained environment, we examine how the uncertainty of the snapped trajectory varies with path length. We demonstrate that as new segments are added to a path, the number of possibilities for earlier segments is monotonically non-increasing. Applications of this work in an urban setting are discussed, as well as future plans to develop a formal theory of odometry-based map-matching.

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ICRA Conference 2015 Conference Paper

Wireless map-based handoffs for mobile robots

Richard Wang
Matthew K. Mukerjee
Manuela Veloso
Srinivasan Seshan

Most wireless solutions today are centered around people-centric devices like laptops and cell phones that are insufficient for mobile robots. The key difference is that people-centric devices use wireless connectivity in bursts under primarily stationary settings while mobile robots continuously transmit data even while moving. When mobile robots use existing wireless solutions, it results in intolerable and seemingly random interruptions in wireless connectivity when moving [1]. These wireless issues stem from suboptimal switching across wireless infrastructure access points (APs), also called AP handoffs. These poor handoff decisions are due to stateless handoff algorithms that make wireless decisions solely from immediate and noisy scans of surrounding wireless conditions. In this paper, we propose to overcome these motion-based wireless connectivity issues for autonomous robots using highly informed handoff algorithms that combine fine-grain wireless maps with accurate robot localization. Our results show significant wireless performance improvements for continuously moving robots in real environments without any modifications to the wireless infrastructure.

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ICRA Conference 2014 Conference Paper

O-Snap: Optimal snapping of odometry trajectories for route identification

Richard Wang
Manuela Veloso
Srinivasan Seshan

An increasing number of wearable and mobile devices are capable of automatically sensing and recording rich information about the surrounding environment. To make use of such data, it is desirable for each data point to be matched with its corresponding spatial location. We focus on using the trajectory from a device's odometry sensors that reveal changes in motion over time. Our goal is to recover the route traversed, which we will define as a sequence of revisitable positions. Dead reckoning, which computes the device's route from its odometry trajectory, is known to suffer from significant drift over time. We aim to overcome drift errors by reshaping the odometry trajectory to fit the constraints of a given topological map and sensor noise model. Prior works use iterative search algorithms that are susceptible to local maximas [15], which means that they can be misled when faced with ambiguous decisions. In contrast, our algorithm is able to find the set of all routes within the given constraints. This also reveals if there are multiple routes that are similarly likely. We can then rank them and select the optimal route that is most likely to be the actual route. We also show that the algorithm can be extended to recover routes even in the presence of topological map errors. We evaluate our algorithm by recovering all routes traversed by a wheeled robot covering over 9 kilometers from its odometry sensor data.

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ICRA Conference 2013 Conference Paper

Multi-robot information sharing for complementing limited perception: A case study of moving ball interception

Richard Wang
Manuela Veloso
Srinivasan Seshan

Poor sensor data because of uncertainty and hardware limitations results in a robot misinterpreting the state of its surrounding environment, leading to bad decisions and eventually failure to successfully perform its desired tasks. These limitations can be overcome if a teammate robot with a better view shares its visual information. Our work aims to investigate why current approaches fail to effectively use teammate sensor data, propose an alternative where a teammate helps to better capture the state of the environment, and demonstrate that the robot can make better decisions when a teammate shares its perceptual data. Raw teammate sensor data is not meaningful unless provided a relative, geometric transform to place this data within another robot's own egocentric coordinates. There are few approaches that are able to discover this relative localization accurately in sparse environments while remaining computationally light. Our approach addresses these limitations by accumulating correspondence matches of objects over time from the overlapping views of two stationary robots to compute an accurate relative localization. We evaluate the benefits of teammate sensor data used with our computed relative localization with a challenging, time critical task where the robot's cameras alone are lacking. Our empirical results with two coordinating robots indicates that our approach is able to successfully take advantage of teammate robots with a better view within the challenging physical and hardware constraints of our robots.

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