Event cameras excel in high temporal resolution and dynamic range but suffer from dense noise in rainy conditions. Existing event deraining methods face trade-offs between temporal precision, deraining effectiveness, and computational efficiency. In this paper, we propose PRE-Mamba, a novel point-based event camera deraining framework that fully exploits the spatiotemporal characteristics of raw event and rain. Our framework introduces a 4D event cloud representation that integrates dual temporal scales to preserve high temporal precision, a Spatio-Temporal Decoupling and Fusion module (STDF) that enhances deraining capability by enabling shallow decoupling and interaction of temporal and spatial information, and a Multi-Scale State Space Model (MS3M) that captures deeper rain dynamics across dual-temporal and multi-spatial scales with linear computational complexity. Enhanced by frequency-domain regularization, PRE-Mamba achieves superior performance (0.95 SR, 0.91 NR, and 0.4s/M events) with only 0.26M parameters on EventRain-27K, a comprehensive dataset with labeled synthetic and real-world sequences. Moreover, our method generalizes well across varying rain intensities, viewpoints, and even snowy conditions.

MAV (Micro Aerial Vehicle) requires landing on a docking platform for recharging during or after missions due to their limited energy capacity. Inspired by biological tactile sensing, we propose a proprioceptive sensing system that allows MAV to "touch", recognize, and locate the landing platform even when visual or other positioning systems are not functioning properly. We leverage a physical phenomenon: as the MAV approaches a beneath obstacle, it experiences attitude disturbances caused by the airflow generated by the rotor’s reflections from the ground. By employing traditional signal processing and learning-based techniques to analyze signals from the IMU (Inertial Measurement Unit) and motors, the MAV can sense the edges of the platform and further calculate the precise landing coordinates. With a power consumption of less than 40 mW, our system achieves an edge detection error of less than 2 cm and a landing success rate exceeding 90%.CCS CONCEPTS• Applied computing → Aerospace; • Computing methodologies → Machine learning approaches; • Computer systems organization → Sensors and actuators.

Accurately localizing high-speed dynamic objects in 3D space with low latency is crucial for various robotic applications. Current methods face challenges due to extended exposure times and limited sensor resolution, hindering precise object detection and localization. Event cameras, known for their high temporal resolution and asynchronous nature, offer a promising solution. To leverage the potential of the event camera, we propose EventTracker, a novel framework that integrates event and depth measurements for precise and low-latency 3D localization and tracking of the high-speed dynamic object. EventTracker incorporates a collaborative object detection and tracking algorithm optimized for both event and depth data, overcoming detection and registration challenges. Additionally, a graph-instructed optimization algorithm enhances accuracy by fusing heterogeneous sensor data effectively. Experimental evaluation in dynamic environments demonstrates significant improvements in localization performance compared to baseline methods.

Event cameras excel in high-speed and high-dynamic-range scenarios but are highly sensitive to rain, which introduces significant noise while also revealing detailed rain features. This paper introduces a novel Event-based Rain-Background Decomposition Network that integrates Spiking Neural Networks (SNNs) and Convolutional Neural Networks (CNNs). By "Distilling Rain," we reconstruct a rain-free background for downstream tasks, and by "Collecting Rain," we extract the physical characteristics of rain. Experimental evaluations demonstrate the network's effectiveness in both background reconstruction and rain modeling. This work extends the capabilities of event cameras by mitigating the adverse effects of rain while also leveraging rain-induced noise to extract valuable environmental data, enhancing their utility in both challenging weather conditions and detailed environmental analysis.

Drone-based rapid and accurate environmental edge detection is highly advantageous for tasks such as disaster relief and autonomous navigation. Current methods, using radar or cameras, raise deployment costs and burden lightweight drones with high computational demands. In this paper, we propose AirTouch, a system that transforms the ground effect from a stability "foe" in traditional flight control views, into a "friend" for accurate and efficient edge detection. Our key insight is that analyzing drone sensor readings and flight commands allows us to detect ground effect changes. Such changes typically indicate the drone flying over an edge, making this information valuable for edge detection. We approach this insight through theoretical analysis, algorithm design, and implementation, fully leveraging the ground effect as a new sensing modality without compromising drone flight stability, thereby achieving accurate and efficient scene edge detection. Extensive evaluations demonstrate that our system achieves a high detection accuracy with mean detection distance errors of 0.051m, outperforming the baseline performance by 86%.

In this work, we tackle 6-DoF grasp detection for transparent and specular objects, which is an important yet challenging problem in vision-based robotic systems, due to the failure of depth cameras in sensing their geometry. We, for the first time, propose a multiview RGB-based 6-DoF grasp detection network, GraspNeRF, that leverages the generalizable neural radiance field (NeRF) to achieve material-agnostic object grasping in clutter. Compared to the existing NeRF-based 3-DoF grasp detection methods that rely on densely captured input images and time-consuming per-scene optimization, our system can perform zero-shot NeRF construction with sparse RGB inputs and reliably detect 6-DoF grasps, both in real-time. The proposed framework jointly learns generalizable NeRF and grasp detection in an end-to-end manner, optimizing the scene representation construction for the grasping. For training data, we generate a large-scale photorealistic domain-randomized synthetic dataset of grasping in cluttered tabletop scenes that enables direct transfer to the real world. Our extensive experiments in synthetic and real-world environments demonstrate that our method significantly outperforms all the baselines in all the experiments while remaining in real-time. Project page can be found at https://pku-epic.github.io/GraspNeRF.