Vision-Aided Inertial Navigation

This document discloses, among other things, a system and method for implementing an algorithm to determine pose, velocity, acceleration or other navigation information using feature tracking data. The algorithm has computational complexity that is linear with the number of features tracked

Analytically-selected multi-hypothesis incremental MAP estimation

In this paper, we introduce an efficient maximum a posteriori (MAP) estimation algorithm, which effectively tracks multiple most probable hypotheses. In particular, due to multimodal distributions arising in most nonlinear problems, we employ a bank of MAP to track these modes (hypotheses). The key idea is that we analytically determine all the posterior modes for the current state at each time step, which are used to generate highly probable hypotheses for the entire trajectory. Moreover, since it is expensive to solve the MAP problem sequentially over time by an iterative method such as Gauss-Newton, in order to speed up its solution, we reuse the previous computations and incrementally update the square-root informationmatrix at every time step, while batch relinearization is performed only periodically or as needed.United States. Office of Naval Research (Grant N00014-10-1-0936)United States. Office of Naval Research (Grant N00014-11-1-0688)United States. Office of Naval Research (Grant N00014-12-10020)National Science Foundation (U.S.) (IIS-0643680

Stochastic cloning: a generalized framework for processing relative state measurements

Introduces a generalized framework, termed "stochastic cloning," for processing relative state measurements within a Kalman filter estimator. The main motivation and application for this methodology is the problem of fusing displacement measurements with position estimates for mobile robot localization. Previous approaches have ignored the developed interdependencies (cross-correlation terms) between state estimates of the same quantities at different time instants. By directly expressing relative state measurements in terms of previous and current state estimates, the effect of these crosscorrelation terms on the estimation process is analyzed and considered during updates. Simulation and experimental results validate this approach

Surface Normal Estimation of Tilted Images via Spatial Rectifier

In this paper, we present a spatial rectifier to estimate surface normals of tilted images. Tilted images are of particular interest as more visual data are captured by arbitrarily oriented sensors such as body-/robot-mounted cameras. Existing approaches exhibit bounded performance on predicting surface normals because they were trained using gravity-aligned images. Our two main hypotheses are: (1) visual scene layout is indicative of the gravity direction; and (2) not all surfaces are equally represented by a learned estimator due to the structured distribution of the training data, thus, there exists a transformation for each tilted image that is more responsive to the learned estimator than others. We design a spatial rectifier that is learned to transform the surface normal distribution of a tilted image to the rectified one that matches the gravity-aligned training data distribution. Along with the spatial rectifier, we propose a novel truncated angular loss that offers a stronger gradient at smaller angular errors and robustness to outliers. The resulting estimator outperforms the state-of-the-art methods including data augmentation baselines not only on ScanNet and NYUv2 but also on a new dataset called Tilt-RGBD that includes considerable roll and pitch camera motion.Comment: 16 page

Weighted line fitting algorithms for mobile robot map building and efficient data representation

This paper presents an algorithm to find the line-based map that best fits sets of two-dimensional range scan data. To construct the map, we first provide an accurate means to fit a line segment to a set of uncertain points via maximum likelihood formalism. This scheme weights each point's influence on the fit according to its uncertainty, which is derived from sensor noise models. We also provide closed-form formulas for the covariance of the line fit, along with methods to transform line coordinates and covariances across robot poses. A Chi-squared based criterion for "knitting" together sufficiently similar lines can be used to merge lines directly (as we demonstrate) or as part of the framework for a line-based SLAM implementation. Experiments using a Sick LMS-200 laser scanner and a Nomad 200 mobile robot illustrate the effectiveness of the algorithm

Ant-inspired navigation in unknown environments

In contrast to most other ant species, desert ants (Cataglyphis fortis) donot use pheromones to mark their path. When returning from a foraging trip to their nest they navigate both by pathintegration and by visual landmarks. An egocentric navigation system based on path integration alone su ers from two major pitfalls: 1. It must run uninterrupted as long as the animal moves and 2. It is inherently susceptible to cumulativenavigation errors. Hence, if the animal visited an area repeatedly, itisadvantageous to take an occasional positional x by acquiring landmark-based geocentered (alocentric) information. Indeed desert ants and honeybees use such information in addition to path integration. On a familiar route, when ants can steer by visual landmarks, they adopt a xed and often circuitous path consisting of several separate segments that point in di erent directions. Suc