The capability to extract task specific, semantic information from raw sensory data is a crucial requirement for many applications of mobile robotics. Autonomous inspection of critical infrastructure with Unmanned Aerial Vehicles (UAVs), for example, requires precise navigation relative to the structure that is to be inspected. Recently, Artificial Intelligence (AI)-based methods have been shown to excel at extracting semantic information such as 6 degree-of-freedom (6-DoF) poses of objects from images. In this paper, we propose a method combining a state-of-the-art AI-based pose estimator for objects in camera images with data from an inertial measurement unit (IMU) for 6-DoF multi-object relative state estimation of a mobile robot. The AI-based pose estimator detects multiple objects of interest in camera images along with their relative poses. These measurements are fused with IMU data in a state-of-the-art sensor fusion framework. We illustrate the feasibility of our proposed method with real world experiments for different trajectories and number of arbitrarily placed objects. We show that the results can be reliably reproduced due to the self-calibrating capabilities of our approach.

Collaboratively estimating the state of two robots under communication constraints is challenging regarding computational complexity and statistical optimality. Previous work only achieves practical solutions by either disregarding parts of the measurements or imposing a communication overhead, being non-optimal or not entirely distributed, respectively. In this work, we present a centralized-equivalent but dis-tributed approach for pairwise state estimation where two agents only communicate when they meet. Our approach utilizes elements from wave scattering theory to efficiently and consistently summarize (pre-compute) past estimator information (i.e., state evolution and uncertainty) between encounters of two agents. This summarized information is then used in a joint correction step taking into account all past information of each agent in a statistically correct way. This novel approach enables us to distribute the pre-computations of both state evolution and uncertainties on the agents and reconstruct the centralized-equivalent system estimate with very few computations once the agents meet again while still applying all measurements from both agents on both estimates upon encounter. We compare our approach on a real-world dataset against a state of the art collaborative state estimation approach.

Suspended Cable-Driven Parallel Robots (SCDPR) have intriguing capabilities on large scales but still have open challenges in precisely estimating the end-effector pose. The cables exhibit a downward curved shape, also known as cable sag which needs to be accounted for in the pose estimation. The catenary equations can accurately describe this phenomenon but are only accurate in equilibrium conditions. Thus, pose estimation for large-scale SCDPR in dynamic motion is an open challenge. This work proposes a real-time pose estimation algorithm for dynamic trajectories of SCDPRs, which is accurate over large areas. We present a novel approach that considers cable sag to reduce the estimation error for large scales while also employing an Inertial Measurement Unit (IMU) to improve estimation accuracy for dynamic motion. Our approach reduces the RMSE to less than a third compared to standard methods not considering cable sag. Similarly, the inclusion of the IMU reduces the RMSE in dynamic situations by 40% compared to non-IMU aided approaches considering cable sag. Further-more, we evaluate our Extended Kalman Filter (EKF) based algorithm on a real system with ground truth pose information.

Sensor fusion systems merging (multiple) delayed sensor signals through a statistical approach are challenging setups, particularly for resource constrained platforms. For statistical consistency, one would be required to keep an appropriate history, apply the correcting signal at the given time stamp in the past, and re-apply all information received until the present time. This re-calculation becomes impractical (the bottleneck being the re-propagation of the covariance matrices for estimator consistency) for platforms with multiple sensors/states and low compute power.This work presents a novel approach for consistent covariance pre-integration allowing delayed sensor signals to be incorporated in a statistically consistent fashion with very low complexity. We leverage recent insights in Invariant Extended Kalman Filters (IEKF) and their log-linear, state independent error propagation together with insights from the scattering theory to mimic the re-calculation process as a medium through which we can propagate waves (covariance information in this case) in single operation steps. We support our findings in simulation and with real data.

Delay compensation in filter based sensor fusion frameworks for multiple sensors with varying delays and different rates quickly results in large computational overhead should the delayed measurements be incorporated in a statistically meaningful way. Even more so if high rate propagation sensors (e.g. IMU) are used. This work presents an approach to implement such frameworks with significant complexity reduction compared to standard implementations. We set particular focus on the state covariance propagation as this chain of re-computations (i.e. FPFT+ Q per propagation step) upon a delayed update is the dominant bottleneck. We draw our inspiration from the scattering theory and propose a method which projects the idea of wave propagation to an efficient concatenation of covariance propagation steps between filter updates. Through this approach, we reach a speed-up of more than a factor of 10 for the covariance propagation and render the computational complexity independent of the number of propagation steps between filter updates. We evaluated our method in simulation and with real data.

Many of today's Visual-Inertial Odometry (VIO)frameworks work well under regular motion but have issues and need special treatment under special motion. Here, special does not imply bad or corrupted data but stands for increased difficulty to treat clean data. Common special motion for VIO are large feature displacement due to fast motion close to a scene and zero motion phases not providing sufficient baseline. In this paper we present a feature and frame selection approach which seamlessly handles all motion scenarios without the need of (error prone)motion case identification and subsequent case-specific heuristics. We further show that this approach allows to eliminate features in the state vector (persistent features)altogether while still being able to inherently handle zero motion phases. This reduces computational complexity while maintaining the ability to hover in place. We integrate our frame selection approach into our own VIO algorithm and compare its performance against three state-of-the-art algorithms with real data on a real platform. While our approach shows slightly higher global drift it is the only algorithm that can reliably estimate the pose over a large motion spectrum from fast scale change down to zero motion.