983 research outputs found
Efficient Constellation-Based Map-Merging for Semantic SLAM
Data association in SLAM is fundamentally challenging, and handling ambiguity
well is crucial to achieve robust operation in real-world environments. When
ambiguous measurements arise, conservatism often mandates that the measurement
is discarded or a new landmark is initialized rather than risking an incorrect
association. To address the inevitable `duplicate' landmarks that arise, we
present an efficient map-merging framework to detect duplicate constellations
of landmarks, providing a high-confidence loop-closure mechanism well-suited
for object-level SLAM. This approach uses an incrementally-computable
approximation of landmark uncertainty that only depends on local information in
the SLAM graph, avoiding expensive recovery of the full system covariance
matrix. This enables a search based on geometric consistency (GC) (rather than
full joint compatibility (JC)) that inexpensively reduces the search space to a
handful of `best' hypotheses. Furthermore, we reformulate the commonly-used
interpretation tree to allow for more efficient integration of clique-based
pairwise compatibility, accelerating the branch-and-bound max-cardinality
search. Our method is demonstrated to match the performance of full JC methods
at significantly-reduced computational cost, facilitating robust object-based
loop-closure over large SLAM problems.Comment: Accepted to IEEE International Conference on Robotics and Automation
(ICRA) 201
Perfect countably infinite Steiner triple systems
We use a free construction to prove the existence of perfect Steiner triple systems on a countably infinite point set. We use a specific countably infinite family of partial Steiner triple systems to start the construction, thus yielding 2ℵ0 non-isomorphic perfect systems
Complexity Analysis and Efficient Measurement Selection Primitives for High-Rate Graph SLAM
Sparsity has been widely recognized as crucial for efficient optimization in
graph-based SLAM. Because the sparsity and structure of the SLAM graph reflect
the set of incorporated measurements, many methods for sparsification have been
proposed in hopes of reducing computation. These methods often focus narrowly
on reducing edge count without regard for structure at a global level. Such
structurally-naive techniques can fail to produce significant computational
savings, even after aggressive pruning. In contrast, simple heuristics such as
measurement decimation and keyframing are known empirically to produce
significant computation reductions. To demonstrate why, we propose a
quantitative metric called elimination complexity (EC) that bridges the
existing analytic gap between graph structure and computation. EC quantifies
the complexity of the primary computational bottleneck: the factorization step
of a Gauss-Newton iteration. Using this metric, we show rigorously that
decimation and keyframing impose favorable global structures and therefore
achieve computation reductions on the order of and , respectively,
where is the pruning rate. We additionally present numerical results
showing EC provides a good approximation of computation in both batch and
incremental (iSAM2) optimization and demonstrate that pruning methods promoting
globally-efficient structure outperform those that do not.Comment: Pre-print accepted to ICRA 201
Improving the efficiency of Bayesian inverse reinforcement learning
Inverse reinforcement learning (IRL) is the task of learning the reward function of a Markov Decision Process (MDP) given knowledge of the transition function and a set of expert demonstrations. While many IRL algorithms exist, Bayesian IRL [1] provides a general and principled method of reward learning by casting the problem in the Bayesian inference framework. However, the algorithm as originally presented suffers from several inefficiencies that prohibit its use for even moderate problem sizes. This paper proposes modifications to the original Bayesian IRL algorithm to improve its efficiency and tractability in situations where the state space is large and the expert demonstrations span only a small portion of it. The key insight is that the inference task should be focused on states that are similar to those encountered by the expert, as opposed to making the naive assumption that the expert demonstrations contain enough information to accurately infer the reward function over the entire state space. A modified algorithm is presented and experimental results show substantially faster convergence while maintaining the solution quality of the original method.United States. Office of Naval Research (Science of Autonomy Program Contract N000140910625)
Institutional Investors, Political Connections and Analyst Following in Malaysia
We examine the association between institutional ownership, political connections, and analyst following in Malaysia from 1999 to 2009. Based on 940 firm-year observations, we document a positive relation between institutional ownership, particularly by Employees Provident Fund (EPF), and analyst following, thus supporting the governance role that institutional investors play in promoting corporate transparency. However, there is no evidence that political connections matter to analyst following. The monitoring role of institutional investors, including EPF, does not appear to be any different between politically connected and non-connected firms
Lightweight infrared sensing for relative navigation of quadrotors
A lightweight solution for estimating position and velocity relative to a known marker is presented. The marker consists of three infrared (IR) LEDs in a fixed pattern. Using an IR camera with a 100 Hz update rate, the range and bearing to the marker are calculated. This information is then fused with inertial sensor information to produce state estimates at 1 kHz using a sigma point Kalman filter. The computation takes place on a 14 gram custom autopilot, yielding a lightweight system for generating high-rate relative state information. The estimation scheme is compared to data recorded with a motion capture system.National Science Foundation (U.S.) (Graduate Research Fellowship under Grant No. 0645960
Robust Trajectory Planning for Autonomous Parafoils under Wind Uncertainty
A key challenge facing modern airborne delivery systems, such as parafoils, is the ability to accurately and consistently deliver supplies into di cult, complex terrain. Robustness is a primary concern, given that environmental wind disturbances are often highly uncertain and time-varying, coupled with under-actuated dynamics and potentially narrow drop zones. This paper presents a new on-line trajectory planning algorithm that enables a large, autonomous parafoil to robustly execute collision avoidance and precision landing on mapped terrain, even with signi cant wind uncertainties. This algorithm is designed to handle arbitrary initial altitudes, approach geometries, and terrain surfaces, and is robust to wind disturbances which may be highly dynamic throughout the terminal approach. Explicit, real-time wind modeling and classi cation is used to anticipate future disturbances, while a novel uncertainty-sampling technique ensures that robustness to possible future variation is e ciently maintained. The designed cost-to-go function enables selection of partial paths which intelligently trade o between current and reachable future states. Simulation results demonstrate that the proposed algorithm reduces the worst-case impact of wind disturbances relative to state-of-the-art approaches.Charles Stark Draper Laborator
Collision Probabilities for Continuous-Time Systems Without Sampling [with Appendices]
Demand for high-performance, robust, and safe autonomous systems has grown
substantially in recent years. Fulfillment of these objectives requires
accurate and efficient risk estimation that can be embedded in core
decision-making tasks such as motion planning. On one hand, Monte-Carlo (MC)
and other sampling-based techniques can provide accurate solutions for a wide
variety of motion models but are cumbersome to apply in the context of
continuous optimization. On the other hand, "direct" approximations aim to
compute (or upper-bound) the failure probability as a smooth function of the
decision variables, and thus are widely applicable. However, existing
approaches fundamentally assume discrete-time dynamics and can perform
unpredictably when applied to continuous-time systems operating in the real
world, often manifesting as severe conservatism. State-of-the-art attempts to
address this within a conventional discrete-time framework require additional
Gaussianity approximations that ultimately produce inconsistency of their own.
In this paper we take a fundamentally different approach, deriving a risk
approximation framework directly in continuous time and producing a lightweight
estimate that actually improves as the discretization is refined. Our
approximation is shown to significantly outperform state-of-the-art techniques
in replicating the MC estimate while maintaining the functional and
computational benefits of a direct method. This enables robust, risk-aware,
continuous motion-planning for a broad class of nonlinear, partially-observable
systems.Comment: To appear at RSS 202
Batch-iFDD for representation expansion in large MDPs
Matching pursuit (MP) methods are a promising class of feature construction algorithms for value function approximation. Yet existing MP methods require creating a pool of potential features, mandating expert knowledge or enumeration of a large feature pool, both of which hinder scalability. This paper introduces batch incremental feature dependency discovery (Batch-iFDD) as an MP method that inherits a provable convergence property. Additionally, Batch-iFDD does not require a large pool of features, leading to lower computational complexity. Empirical policy evaluation results across three domains with up to one million states highlight the scalability of Batch-iFDD over the previous state of the art MP algorithm.United States. Office of Naval Research (Grant N00014-07-1-0749)United States. Office of Naval Research (Grant N00014-11-1-0688
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