This paper proposes a simple yet effective approach to learn visual features online for improving loop-closure detection and place recognition, based on bag-of-words frameworks. The approach learns a codeword in the bag-of-words model from a pair of matched features from two consecutive frames, such that the codeword has temporally-derived perspective invariance to camera motion. The learning algorithm is efficient: the binary descriptor is generated from the mean image patch, and the mask is learned based on discriminative projection by minimizing the intra-class distances among the learned feature and the two original features. A codeword is generated by packaging the learned descriptor and mask, with a masked Hamming distance defined to measure the distance between two codewords. The geometric properties of the learned codewords are then mathematically justified. In addition, hypothesis constraints are imposed through temporal consistency in matched codewords, which improves precision. The approach, integrated in an incremental bag-of-words system, is validated on multiple benchmark data sets and compared to state-of-the-art methods. Experiments demonstrate improved precision/recall outperforming state of the art with little loss in runtime.

Not all measured features in SLAM/SfM contribute to accurate localization during the estimation process, thus it is sensible to utilize only those that do. This paper describes a method for selecting a subset of features that are of high utility for localization in the SLAM/SfM estimation process. It is derived by examining the observability of SLAM and, being complimentary to the estimation process, it easily integrates into existing SLAM systems. The measure of estimation utility is formulated with temporal and instantaneous observability indices. Efficient computation strategies for the observability indices are described based on incremental singular value decomposition (SVD) and greedy selection for the temporal and instantaneous observability indices, respectively. The greedy selection is near-optimal since the observability index is (approximately) submodular. The proposed method improves localization and data association. Controlled synthetic experiments with ground truth demonstrate the improved localization accuracy, and real-time SLAM experiments demonstrate the improved data association.

The scales on the skin of a snake are an integral part of the snake's locomotive capabilities. It stands to reason that the integration of scales into the design of robotic snakes would open new properties to exploit. In this work, we present a robotic snake design that incorporates rigid scales in the casing of each module. To validate the impact of the scales, locomotion is tested under three conditions: with scales, covered in cloth with scales, and covered in cloth without scales. The performance of the snake robot, in each of the aforementioned scenarios, is evaluated based on its forward displacement while executing each of two pre-programmed gaits: inchworm and lateral undulation. The lateral undulation gait is tested under two additional conditions: pitched and un-pitched. Tracks of the experimental runs are presented followed by a statistical analysis demonstrating an increase in locomotive performance when incorporating scales in the chassis design.

This paper utilizes system observability to guide monocular SLAM. Instead of providing all measured features then performing data-driven outlier rejection (such as with RANSAC), we propose to identify only the minimal subset of features which form an optimally observable SLAM subsystem for localization. Modeling the SLAM system as a discrete time system with piece-wise linear SE〈3〉 motion, complete observability conditions are derived and a means to test the observability conditioning of candidate feature point groupings is proposed. Based on the conditioning, an efficient algorithm for picking the optimally observable feature subset is derived by incorporating the image geometric measures. The proposed monocular SLAM algorithm, called Optimally Observable and Minimal Cardinality (OOMC) SLAM is formulated as an EKF process. OOMC SLAM is first validated using a 6-DOF localization experiment; the results demonstrate accuracy comparable to the state-of-art SLAM algorithm with significantly improved computational efficiency. A longer sequence on a 620-meter trajectory is also tested. The algorithm achieves 0.9178% relative error against the GPS ground truth.