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Dive into the research topics where Hans Jacob S. Feder is active.

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Featured researches published by Hans Jacob S. Feder.


The International Journal of Robotics Research | 1999

Adaptive Mobile Robot Navigation and Mapping

Hans Jacob S. Feder; John J. Leonard; Christopher M. Smith

The task of building a map of an unknown environment and concurrently using that map to navigate is a central problem in mobile robotics research. This paper addresses the problem of how to perform concurrent mapping and localization (CML) adaptively using sonar. Stochastic mapping is a feature-based approach to CML that generalizes the extended Kalman filter to incorporate vehicle localization and environmental mapping. The authors describe an implementation of stochastic mapping that uses a delayed nearest neighbor data association strategy to initialize new features into the map, match measurements to map features, and delete out-of-date features. The authors introduce a metric for adaptive sensing that is defined in terms of Fisher information and represents the sum of the areas of the error ellipses of the vehicle and feature estimates in the map. Predicted sensor readings and expected dead-reckoning errors are used to estimate the metric for each potential action of the robot, and the action that yields the lowest cost (i.e., the maximum information) is selected. This technique is demonstrated via simulations, in-air sonar experiments, and underwater sonar experiments. Results are shown for (1) adaptive control of motion and (2) adaptive control of motion and scanning. The vehicle tends to explore selectively different objects in the environment. The performance of this adaptive algorithm is shown to be superior to straight-line motion and random motion.


ISRR | 2000

A Computationally Efficient Method for Large-Scale Concurrent Mapping and Localization

John J. Leonard; Hans Jacob S. Feder

Decoupled stochastic mapping (DSM) is a computationally efficient approach to large-scale concurrent mapping and localization. DSM reduces the computational burden of conventional stochastic mapping by dividing the environment into multiple overlapping submap regions, each with its own stochastic map. Two new approximation techniques are utilized for transferring vehicle state information from one submap to another, yielding a constant-time algorithm whose memory requirements scale linearly with the size of the operating area. The performance of two different variations of the algorithm is demonstrated through simulations of environments with 110 and 1200 features. Experimental results are presented for an environment with 93 features using sonar data obtained in a 3 by 9 by 1 meter testing tank.


IEEE Journal of Oceanic Engineering | 2001

Decoupled stochastic mapping [for mobile robot & AUV navigation]

John J. Leonard; Hans Jacob S. Feder

This paper describes decoupled stochastic mapping (DSM), a new computationally efficient approach to large-scale concurrent mapping and localization (CML). DSM reduces the computational burden of conventional stochastic mapping by dividing the environment into multiple overlapping submap regions, each with its own stochastic map. Two new approximation techniques are utilized for transferring vehicle state information from one submap to another, yielding a constant-time algorithm whose memory requirements scale linearly with the number of submaps. The approach is demonstrated via simulations and experiments. Simulation results are presented for the case of an autonomous underwater vehicle navigating in an unknown environment with 110 and 1200 features using simulated observations of point features by a forward look sonar. Empirical tests are used to examine the consistency of the error bounds calculated by the different methods. Experimental results are also presented for an environment with 93 features using sonar data obtained in a 3 by 9 by 1 m testing tank.


international conference on robotics and automation | 1997

Real-time path planning using harmonic potentials in dynamic environments

Hans Jacob S. Feder; Jean-Jacques E. Slotine

Motivated by fluid analogies, artificial harmonic potentials can eliminate local minima problems in robot path planning. In this paper, simple analytical solutions to planar harmonic potentials are derived using tools from fluid mechanics, and are applied to two-dimensional planning among multiple moving obstacles. These closed-form solutions enable real-time computation to be readily achieved.


Robotica | 2001

Stochastic mapping using forward look sonar

John L. Leonard; Robert N. Carpenter; Hans Jacob S. Feder

This paper investigates the problem of concurrent mapping and localization (CML) using forward look sonar data. Results are presented from processing of an oceanic data set from an 87 kHz US Navy forward look imaging sonar using the stochastic mapping method for CML. The goal is to detect objects on the seabed, map their locations, and concurrently compute an improved trajectory for the vehicle. The resulting trajectory is compared with position estimates computed with an inertial navigation system and Doppler velocity sonar. The results demonstrate the potential of concurrent mapping and localization algorithms to satisfy the navigation requirements of undersea vehicles equipped with forward look sonar.


oceans conference | 1998

Adaptive sensing for terrain aided navigation

Hans Jacob S. Feder; John J. Leonard; Christopher M. Smith

Demonstrates experiments for performing adaptive terrain aided navigation in the context of autonomous underwater vehicles (AUVs) equipped with sonar. The experiments were conducted using a 675 kHz sector scan sonar mounted on a planar robotic positioning system in a 3.0 by 9.0 by 1.0 meter testing tank, enabling controlled and repeatable scenarios. The objective of the adaptive stochastic mapping algorithm is to enable feature-based terrain aided navigation of AUVs in environments where no a priori map is available. The approach assumes that distinctive, point-like features can be extracted from vehicle sensor data. A dead-reckoning error model is incorporated to simulate an AUVs navigation system error growth. An adaptation step based on maximizing the Fisher information gained by the next reaction of the sensor is coupled with the stochastic mapping algorithm to yield more precise position estimates for features in the environment and the vehicle.


intelligent robots and systems | 1998

Adaptive concurrent mapping and localization using sonar

Hans Jacob S. Feder; John J. Leonard; Christopher M. Smith

In order to create a truly autonomous vehicle, the task of concurrent mapping and localization (CML) in an a priori unknown environment is an important problem. Traditionally, the task of CML has been separated from the vehicles motion and sensing strategies. We introduce a method for adaptive concurrent mapping and localization in unknown environments using a scanning sonar sensor. This method maximizes the information gained by the next action of the robot, given the space of available actions. The viability of the approach is shown in simulation and experiments. Results are shown for both adaptive control of motion and adaptive control of motion and sensing. Improved performance is demonstrated in comparison to straight-line motion and random motion.


conference on decision and control | 1998

Multiple target tracking with navigation uncertainty

Christopher M. Smith; Hans Jacob S. Feder; John J. Leonard

The goal of concurrent mapping and localization (CML) is for a mobile robot to build a map of an unknown environment while simultaneously using that map to navigate. CML can be considered as a problem of multiple target tracking (MTT) in the presence of navigation uncertainty. Although data association errors can have a catastrophic effect on CML performance, previous approaches to CML, such as stochastic mapping (SM), have either ignored the data association problem, matched features by hand, or used a nearest-neighbor approach. We have developed integrated mapping and navigation (IMAN), a multiple hypothesis approach to CML that generalizes SM to incorporate data association uncertainty and expands multiple hypothesis tracking (MHT) to accommodate navigation error. The paper summarizes IMAN and illustrates its performance for a simulation of an autonomous underwater vehicle (AUV) navigating with a forward-looking sonar.


ieee/oes autonomous underwater vehicles | 1998

Incorporating environmental measurements in navigation

Hans Jacob S. Feder; John J. Leonard; Christopher M. Smith

Extended missions in unknown regions present a significant navigational challenge for autonomous underwater vehicles (AUV). This paper investigates the long-term performance of a concurrent mapping and localization (CML) algorithm for the scenario of an AUV making observations of point features in the environment with a forward look sonar. Simulation results demonstrate that position estimates with long-term bounded errors of a few meters can be achieved under realistic assumptions about the vehicle, its sensors, and the environment. Potential failure modes of the algorithm, such as divergence and map slip, are discussed. CML technology can provide a significant improvement in the navigational capabilities of AUVs and can enable new missions in unmapped regions without reliance on acoustic beacons or surfacing for GPS resets.


ieee/oes autonomous underwater vehicles | 1998

Making difficult decisions autonomously: the impact of integrated mapping and navigation

Christopher M. Smith; John J. Leonard; Hans Jacob S. Feder

The role of navigation is changing. The forces of increased autonomy, less prior knowledge, and larger missions are extending the navigation problem from the requirement of absolute localization to the larger question of context determination. Current technologies are inadequate in the face of such circumstances. The key to an evolved navigation technology begins with the ability to reason, in an integrated way, about the models used to determine vehicle context: physical models, dynamic models, sensor models, and behavior models. The integrated mapping and localization (IMAN) algorithm provides a hybrid estimation scheme to integrate decision-making about navigation events with navigation and mapping. An overview of IMAN is presented, along with an initial analysis of its performance. While IMAN is sensitive to the complexity of ambiguous situations, the algorithm demonstrates superior performance when complexity does not lead to failure. These results are used to examine the emerging set of technological needs for advanced navigation and mapping applications, including map representation, multiscale modeling, map fusion, and cross-model correlation.

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John J. Leonard

Massachusetts Institute of Technology

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Christopher M. Smith

Massachusetts Institute of Technology

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Jean-Jacques E. Slotine

Massachusetts Institute of Technology

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John L. Leonard

Massachusetts Institute of Technology

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Robert N. Carpenter

Naval Undersea Warfare Center

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