In this paper we present BOEM-SLAM, a backend for visual-inertial SLAM systems capable of creating a globally consistent trajectory and map without retaining the entire history of data. By leveraging the hidden Markov model structure, BOEM-SLAM can summarize historical data into sufficient statistics and then discard it. As a data-efficient algorithm, BOEM-SLAM addresses the growing computational costs and storage requirements of the SLAM backend. To demonstrate the performance of our algorithm we compare BOEM-SLAM to other fundamental approaches on both synthetic data and the EuRoC dataset. For evaluation on the EuRoC dataset, we use the open source okvis frontend and apply the Lie group state space representation and visual outlier removal. Overall, BOEM-SLAM shows a considerably lower computation time with comparable estimation performance. For example, the processing time of BOEM-SLAM is 50 times smaller than the optimization-based method using simulated data and 5 times smaller than the optimization-based method in the EuRoC dataset experiments.

Monocular depth inference has gained tremendous attention from researchers in recent years and remains as a promising replacement for expensive time-of-flight sensors, but issues with scale acquisition and implementation overhead still plague these systems. To this end, this work presents an unsupervised learning framework that is able to predict at-scale depth maps and egomotion, in addition to camera intrinsics, from a sequence of monocular images via a single network. Our method incorporates both spatial and temporal geometric constraints to resolve depth and pose scale factors, which are enforced within the supervisory reconstruction loss functions at training time. Only unlabeled stereo sequences are required for training the weights of our single-network architecture, which reduces overall implementation overhead as compared to previous methods. Our results demonstrate strong performance when compared to the current state-of-the-art on multiple sequences of the KITTI driving dataset and can provide faster training times with its reduced network complexity.

Under line-of-sight (LOS) network conditions, multi-input multi-output (MIMO) wireless communications can increase the channel capacity between a team of robots and a multi-antenna array at a stationary base station. This increased capacity can result in greater data throughput, shortening the time necessary to complete channel-limited data aggregation tasks. To take advantage of this higher capacity channel, the robots in the team must be positioned to maximize complex channel orthogonality between each robot and receiver antenna. Using geometrically motivated assumptions, we derive transmitter spacing rules that can be easily be added on to existing path plans to improve backhaul throughput for data offloading from the robot team, with minimal impact on other system objectives. We demonstrate the effectiveness of the approach- both in ideal as well as realistic channels outside the domain of our simplifying assumptions-with numerical examples of robot-coordinated path plans in two example environments, achieving up to 42% improvement in task completion times.

The Non-Anthropomorphic Biped (NABi) has demonstrated agile motion without complex control due to its unique leg alignment in the sagittal plane. This paper presents NABi-S, a small version of NABi. The “S” designation refers to the new spring or “soleus” mechanism used to store energy and stabilize walking. An important feature of NABi-S is its ability to self-stabilize - it can recover from any tipping in the sagittal plane. Using this feature, NABi-S demonstrates stable motion using CPG control, a difficult task to achieve on a biped. In this paper, the soleus prototype is designed and analyzed. An open-loop, CPG algorithm is then implemented, and a parameter space search is performed to optimize robot locomotion.

A robot with the ability to traverse complex duct systems requires a large range of controllable motions as well as the ability to grip the duct walls in vertical shafts. We present a tensegrity robot with two linked tetrahedral frames, each containing a linear actuator, connected by a system of eight actuated cables. The robot climbs by alternately wedging each tetrahedron within the duct and moving one tetrahedron relative to the other. We first introduce our physical prototype, called DuCTT (Duct Climbing Tetrahedral Tensegrity). We next discuss the inverse kinematic control strategy used to actuate the robot and analyze the controller's capabilities within a physics simulation. Finally, we discuss the hardware prototype and compare its performance with simulation.