12 accepted papers
Accurate time synchronization between heterogeneous sensors is crucial for ensuring robust state estimation in multi-sensor fusion systems. Sensor delays often cause discrepancies between the actual time when the event was captured and the time of sensor measurement, leading to temporal misalignment…
Point clouds are a fundamental representation for robotic perception tasks such as localization, mapping, and object pose estimation. However, LiDAR-acquired point clouds are inherently sparse and non-uniform, providing incomplete observations of the underlying geometry. Such sparsity and non-unifor…
Relay robots are crucial for extending communication when a client robot performs long-range missions. However, existing network quality prediction models and relay planning methods often struggle with real-time operation due to their high computational cost and poor adaptability to frequently chang
Relay robots are crucial for extending communication when a client robot performs long-range missions. However, existing network quality prediction models and relay planning methods often struggle with real-time operation due to their high computational cost and poor adaptability to frequently chang…
This paper addresses vision-based autonomous landing of quadrotor drones on moving platforms with uncertain motion. Vision is attractive due to its low weight, low cost, and ability to provide direct relative observations without global reference frames. However, traditional visual landing relies on
Achieving successful scan matching is essential for LiDAR odometry. However, in challenging environments with adverse weather conditions or repetitive geometric patterns, LiDAR odometry performance is degraded due to incorrect scan matching. Recently, the emergence of frequency-modulated continuous…
Accurate time synchronization between heterogeneous sensors is crucial for ensuring robust state estimation in multi-sensor fusion systems. Sensor delays often cause discrepancies between the actual time when the event was captured and the time of sensor measurement, leading to temporal misalignment
This letter proposes a dual-mode planner for localizing gas sources using a mobile sensor in unknown indoor spaces. The complexity of indoor environments creates constraints on search paths, leading to situations where no valid paths can be generated, which are termed as dead end in this letter. The
This paper describes autonomous landing of an unmanned aircraft system on a moving platform using vision and deep reinforcement learning. Landing on the moving platform offers several benefits such as more mission flexibility and reduced flight time. In particular, the end-to-end vision approach (i.
This paper proposes a deep reinforcement learning method for mobile sensors to estimate the properties of the source of the hazardous gas release. The problem of estimating the properties of the released gas is generally termed as the source term estimation (STE) problem. Since the sensor measuremen
This letter presents the trajectory planning of small unmanned aerial vehicles (UAVs) for a communication relay mission in an urban environment. In particular, we focus on predicting the communication strength between air and ground nodes accuratelyto allow relay UAVs to maximize the communication p
This paper proposes the use of a learning approach to predict air-to-ground (A2G) communication strength in support of the communication relay mission using UAVs in an urban environment. To plan an efficient relay trajectory, A2G communication link quality needs to be predicted between the UAV and g…