Therese Joseph

Therese Joseph, Tobias Fischer, Michael Milford

Compared to conventional cameras, event cameras provide a high dynamic range and low latency, offering greater robustness to rapid motion and challenging lighting conditions. Although the potential of event cameras for visual place recognition (VPR) has been established, developing robust VPR frameworks under severe illumination changes remains an open research problem. In this paper, we introduce an ensemble-based approach to event camera place recognition that combines sequence-matched results from multiple event-to-frame reconstructions, VPR feature extractors, and temporal resolutions. Unlike previous event-based ensemble methods, which only utilise temporal resolution, our broader fusion strategy delivers significantly improved robustness under varied lighting conditions (e.g., afternoon, sunset, night), achieving a 57% relative improvement in Recall@1 across day-night transitions. We evaluate our approach on two long-term driving datasets (with 8 km per traverse) without metric subsampling, thereby preserving natural variations in speed and stop duration that influence event density. We also conduct a comprehensive analysis of key design choices, including binning strategies, polarity handling, reconstruction methods, and feature extractors, to identify the most critical components for robust performance. Additionally, we propose a modification to the standard sequence matching framework that enhances performance at longer sequence lengths. To facilitate future research, we will release our codebase and benchmarking framework.

David Howard, Jack O'Connor, Jordan Letchford et al.

The application of granular jamming in soft robotics is a recent and promising new technology offer exciting possibilities for creating higher performance robotic devices. Granular jamming is achieved via the application of a vacuum pressure inside a membrane containing particulate matter, and is particularly interesting from a design perspective, as a myriad of design parameters can potentially be exploited to induce a diverse variety of useful behaviours. To date, the effect of variables such as grain shape and size, as well as membrane material, have been studied as a means of inducing bespoke gripping performance, however the other main contributing factor, membrane morphology, has not been studied due to its particular complexities in both accurate modelling and fabrication. This research presents the first study that optimises membrane morphology for granular jamming grippers, combining multi-material 3D printing and an evolutionary algorithm to search through a varied morphology design space in materio. Entire generations are printed in a single run and gripper retention force is tested and used as a fitness measure. Our approach is relatively scalable, circumvents the need for modelling, and guarantees the real-world performance of the grippers considered. Results show that membrane morphology is a key determinant of gripper performance. Common high performance designs are seen to optimise all three of the main identified mechanisms by which granular grippers generate grip force, are significantly different from a standard gripper morphology, and generalise well across a range of test objects.