Over the past decade, tremendous amount of research activity has focused around the problem of localization in GPS denied environments. Challenges with localization are highlighted in human wearable systems where the operator can freely move through both indoors and outdoors. In this paper, we present a robust method that addresses these challenges using a human wearable system with two pairs of backward and forward looking stereo cameras together with an inertial measurement unit (IMU). This algorithm can run in real-time with 15Hz update rate on a dual-core 2GHz laptop PC and it is designed to be a highly accurate local (relative) pose estimation mechanism acting as the front-end to a Simultaneous Localization and Mapping (SLAM) type method capable of global corrections through landmark matching. Extensive tests of our prototype system so far, reveal that without any global landmark matching, we achieve between 0.5% and 1% accuracy in localizing a person over a 500 meter travel indoors and outdoors. To our knowledge, such performance results with a real time system have not been reported before.

Abstract-This paper proposes a navigation algorithm that provides a low-latency solution while estimating the full nonlinear navigation state. Our approach uses Sliding-Window Factor Graphs, which extend existing incremental smoothing methods to operate on the subset of measurements and states that exist inside a sliding time window. We split the estimation into a fast short-term smoother, a slower but fully global smoother, and a shared map of 3D landmarks. A novel three-stage visual feature model is presented that takes advantage of both smoothers to optimize the 3D landmark map, while minimizing the computation required for processing tracked features in the short-term smoother. This three-stage model is formulated based on the maturity of the estimation of the 3D location of the underlying landmark in the map. Long-range associations are used as global measurements from matured landmarks in the short-term smoother and loop closure constraints in the longterm smoother. Experimental results demonstrate our approach provides highly-accurate solutions on large-scale real data sets using multiple sensors in GPS-denied settings.

Our goal is to create a visual odometry system for robots and wearable systems such that localization accuracies of centimeters can be obtained for hundreds of meters of distance traveled. Existing systems have achieved approximately a 1% to 5% localization error rate whereas our proposed system achieves close to 0.1% error rate, a ten-fold reduction. Traditional visual odometry systems drift over time as the frame-to-frame errors accumulate. In this paper, we propose to improve visual odometry using visual landmarks in the scene. First, a dynamic local landmark tracking technique is proposed to track a set of local landmarks across image frames and select an optimal set of tracked local landmarks for pose computation. As a result, the error associated with each pose computation is minimized to reduce the drift significantly. Second, a global landmark based drift correction technique is proposed to recognize previously visited locations and use them to correct drift accumulated during motion. At each visited location along the route, a set of distinctive visual landmarks is automatically extracted and inserted into a landmark database dynamically. We integrate the landmark based approach into a navigation system with 2 stereo pairs and a low-cost Inertial Measurement Unit (IMU) for increased robustness. We demonstrate that a real-time visual odometry system using local and global landmarks can precisely locate a user within 1 meter over 1000 meters in unknown indoor/outdoor environments with challenging situations such as climbing stairs, opening doors, moving foreground objects etc..

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