Papers

@conference{granados2025kinodynamic,
title = {Kinodynamic Trajectory Following with STELA: Simultaneous Trajectory Estimation & Local Adaptation},
author = {E Granados and S Tangirala and K Bekris},
url = {https://prx-kinodynamic.github.io/projects/stela},
year = {2025},
date = {2025-06-21},
booktitle = {Proceedings of Robotics: Science and Systems (RSS)},
abstract = {State estimation and control are often addressed separately, leading to unsafe execution due to sensing noise, execution errors, and discrepancies between the planning model and reality. Simultaneous control and trajectory estimation using probabilistic graphical models has been proposed as a unified solution to these challenges. Previous work, however, relies heavily on appropriate Gaussian priors and is limited to holonomic robots with linear time-varying models. The current research extends graphical optimization methods to vehicles with arbitrary dynamical models via Simultaneous Trajectory Estimation and Local Adaptation (STELA). The overall approach initializes feasible trajectories using a kinodynamic, sampling-based motion planner. Then, it simultaneously: (i) estimates the past trajectory based on noisy observations, and (ii) adapts the controls to be executed to minimize deviations from the planned, feasible trajectory, while avoiding collisions. The proposed factor graph representation of trajectories in STELA can be applied for any dynamical system given access to first or second-order state update equations, and introduces the duration of execution between two states in the trajectory discretization as an optimization variable. These features provide both generalization and flexibility in trajectory following. In addition to targeting computational efficiency, the proposed strategy performs incremental updates of the factor graph using the iSAM algorithm and introduces a time-window mechanism. This mechanism allows the factor graph to be dynamically updated to operate over a limited history and forward horizon of the planned trajectory. This enables online updates of controls at a minimum of 10Hz. Experiments demonstrate that STELA achieves at least comparable performance to previous frameworks on idealized vehicles with linear dynamics. STELA also directly applies to and successfully solves trajectory following problems for more complex dynamical models. Beyond generalization, simulations assess STELA's robustness under varying levels of sensing and execution noise, while ablation studies highlight the importance of different components of STELA. Real-world experiments validate STELA's practical applicability on a low-cost MuSHR robot, which exhibits high noise and non-trivial dynamics.},
keywords = {Dynamics, Estimation},
pubstate = {published},
tppubtype = {conference}
}

@conference{Apostolopoulos:2012aa,
title = {Integrated Online Localization and Navigation for People with Visual Impairments Using Smart Phones},
author = {I Apostolopoulos and N Fallah and E Folmer and K Bekris},
url = {http://www.cs.rutgers.edu/~kb572/pubs/ICRA12_1454_FI.pdf},
year = {2012},
date = {2012-05-01},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
address = {Minnesota, MN},
abstract = {Indoor localization and navigation systems for individuals with visual impairments (VI) typically rely upon extensive augmentation of the physical space or heavy, expensive sensors; thus, few systems have been adopted. This work de- scribes a system able to guide people with VI through buildings using inexpensive sensors, such as accelerometers, which are available in portable devices like smart phones. The method takes advantage of feedback from the human user, who confirms the presence of landmarks. The system calculates the usertextquoterights location in real time and uses it to provide audio instructions on how to reach the desired destination. Previous work suggested that the accuracy of the approach depended on the type of directions and the availability of an appropriate transition model for the user. A critical parameter for the transition model is the usertextquoterights step length. The current work investigates different schemes for automatically computing the usertextquoterights step length and reducing the dependency of the approach to the definition of an accurate transition model. Furthermore, the direction provision method is able to use the localization estimate and adapt to failed executions of paths by the users. Experiments are presented that evaluate the accuracy of the overall integrated system, which is executed online on a smart phone. Both people with visual impairments, as well as blindfolded sighted people, participated in the experiments. The experiments included paths along multiple floors, that required the use of stairs and elevators.},
keywords = {Estimation},
pubstate = {published},
tppubtype = {conference}
}

@conference{Ladd:2002aa,
title = {Using Wireless Ethernet for Localization},
author = {A Ladd and K Bekris and G Marceau and A Rudys and L Kavraki and D Wallach},
url = {https://ieeexplore.ieee.org/document/1041423},
year = {2002},
date = {2002-09-01},
booktitle = {IEEE/RJS International Conference on Intelligent Robots and Systems (IROS02)},
address = {Lausanne, Switzerland},
abstract = {IEEE 802.11b wireless Ethernet is rapidly becoming the standard for in-building and short-range wireless communication. Many mobile devices such as mobile robots, laptops and PDAs already use this protocol for wireless communication. Many wireless Ethernet cards measure the signal strength of incoming packets. This paper investigates the feasibility of implementing a localization system using this sensor. Using a Bayesian localization framework, we show experiments demonstrating that off-the-shelf wireless hardware can accurately be used for location sensing and tracking with about one meter precision in a wireless-enabled office building.},
keywords = {Estimation},
pubstate = {published},
tppubtype = {conference}
}