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. 1.

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.. 1.

Abstract — A common prerequisite for many vision-based driver assistance systems is the knowledge of the vehicle’s own movement. In this paper we propose a novel approach for estimating the egomotion of the vehicle from a sequence of stereo images. Our method is directly based on the trifocal geometry between image triples, thus no time expensive recovery of the 3-dimensional scene structure is needed. The only assumption we make is a known camera geometry, where the calibration may also vary over time. We employ an Iterated Sigma Point Kalman Filter in combination with a RANSAC-based outlier rejection scheme which yields robust frame-to-frame motion estimation even in dynamic environments. A high-accuracy inertial navigation system is used to evaluate our results on challenging real-world video sequences. Experiments show that our approach is clearly superior compared to other filtering techniques in terms of both, accuracy and run-time. I.

Abstract — Stereo based visual odometry is particularly interesting for affordable all-terrain robots, which would otherwise require expensive inertial navigation systems. A key aspect of the method is the process of matching image features across frames, which tends to produce a considerable amount of outliers, i.e. mismatched features. Outliers are usually handled in the subsequent motion estimation step, by recurring to robust statistical methods (e.g. RANSAC), whose computational cost grows with the probable number of outliers. Other approaches remove the outliers prior to the motion estimation step, guaranteeing a fixed computational cost independent of the number of outliers. This paper departs from previous work on this latter approach by proposing a heuristic algorithm which does not need to generate rotation hypotheses, nor to assume that the robot is moving on a planar surface. This algorithm is part of a wider real-time visual odometry system implemented on a stereo-based all-terrain robot. Experimental results in the physical robot highlight the capabilities of the proposed method. Index Terms — Visual odometry, outliers removal, stereo vision, optic flow, mobile robots.

Abstract — We present and examine a technique for estimating the ego-motion of a mobile robot using memory-based learning and a monocular camera. Unlike other approaches that rely heavily on camera calibration and geometry to compute trajectory, our method learns a mapping from sparse optical flow to platform velocity and turn rate. We also demonstrate an efficient method of computing high-quality sparse optical flow, and techniques for using this sparse optical flow as input to a supervised learning method. We employ a voting scheme of many learners that use subsets of the sparse optical flow to cope with variable dimensionality and reduce the dimensionality of each learner. Finally, we perform experiments in which we examine the learned mapping for visual odometry, investigate the effects of varying the reduced dimensionality of the sparse optical flow state, and quantify the accuracy of two variations of our learner scheme. Our results indicate that our learning scheme estimates monocular visual odometry mainly from points on the ground plane, and reflect to a degree the minimum dimensionality imposed by the problem. In addition, we show that while this memory-based learning method cannot yet estimate ego-motion as accurately as recent geometric methods, it is possible to learn, with no explicit model of camera calibration or scene structure, complicated mappings that take advantage of properties of the camera and the environment. I.

Abstract—Marking-based lane recognition requires an unobstructed view onto the road. In practice however, heavy traffic often constrains the visual field, especially in urban scenarios such as urban crossroads. In this paper we present a novel approach to road mosaicing for dynamic environments. Our method is based on a multistage registration procedure and uses blending techniques. We show that under modest assumptions accurate registration is possible from monocular image sequences. We further demonstrate that fusing visual information from previous frames into the current view can greatly extend the camera’s field of view. I.