Reactive collision avoidance is essential for agile robots navigating complex and dynamic environments, enabling real-time obstacle response. However, this task is inherently challenging because it requires a tight integration of perception, planning, and control, which traditional methods often handle separately, resulting in compounded errors and delays. This paper introduces a novel approach that unifies these tasks into a single reactive framework using solely onboard sensing and computing. Our method combines nonlinear model predictive control with adaptive control barrier functions, directly linking perception-driven constraints to real-time planning and control. Constraints are determined by using a neural network to refine noisy RGB-D data, enhancing depth accuracy, and selecting points with the minimum time-to-collision to prioritize the most immediate threats. To maintain a balance between safety and agility, a heuristic dynamically adjusts the optimization process, preventing overconstraints in real time. Extensive experiments with an agile quadrotor demonstrate effective collision avoidance across diverse indoor and outdoor environments, without requiring environment-specific tuning or explicit mapping.

Human pose, serving as a robust appearance-invariant mid-level feature, has proven to be effective and efficient for human action recognition and intention estimation. Pose features also have a great potential to improve trajectory prediction for the Vulnerable Road User (VRU) in ADAS or automated driving applications. However, the lack of highly diverse and large VRU pose datasets makes a transfer and application to the VRU rather difficult. This paper introduces the Tsinghua-Daimler Urban Pose dataset (TDUP), a large-scale 2D VRU pose image dataset collected in Chinese urban traffic environments from on-board a moving vehicle. The TDUP dataset contains 21k images with more than 90k high-quality, manually labeled VRU bounding boxes with pose keypoint annotations and additional tags. We optimize four state-of-the-art deep learning approaches (AlphaPose, Mask R-CNN, Pose-SSD and PitPaf) to serve as baselines for the new pose estimation benchmark. We further analyze the effect of using large pre-training datasets and different data proportions as well as optional labeled information during training. Our new benchmark is expected to lay the foundation for further VRU pose studies and to empower the development of accurate VRU trajectory prediction methods in complex urban traffic scenes. The dataset (including an evaluation server) is available on www.urbanpose-dataset.com for non-commercial scientific use.