With the development of robotics, ground robots are no longer limited to planar motion. Passive height variation due to complex terrain and active height control provided by special structures on robots require a more general navigation planning framework beyond 2D. Existing methods rarely considers both simultaneously, limiting the capabilities and applications of ground robots. In this paper, we proposed an optimization-based planning framework for ground robots considering both active and passive height changes on the z-axis. The proposed planner first constructs a penalty field for chassis motion constraints defined in$\mathbb{R}^{3}$such that the optimal solution space of the trajectory is continuous, resulting in a high-quality smooth chassis trajectory. Also, by constructing custom constraints in the z-axis direction, it is possible to plan trajectories for different types of ground robots which have z-axis degree of freedom. We performed simulations and real-world experiments to verify the efficiency and trajectory quality of our algorithm.

Government data identifies driver behaviour errors as a factor in 94% of car crashes, and autonomous vehicles (AVs), which avoids risky driver behaviours completely, are expected to reduce the number of road crashes significantly. Thus, one of the central focuses of developing AVs is to ensure safety during navigation. However, in reality, AV safety has been far below its expectation, and so far, no government has allowed for complete autonomous driving without human supervision. This paper proposes a dynamic safe path planning algorithm for AVs with Gaussian process regulated risk map. By reasonably assuming that the output of the object detection and tracking module follows a multi-variate Gaussian distribution, we put forward a safe path planning paradigm with Gaussian process regulated risk map, ensuring safety with high confidence. Both simulation results and in-vehicle tests demonstrate the effectiveness of the proposed algorithm.

Environment understanding, object detection and recognition are crucial skills for robots operating in the real world. In this paper, we propose a Convolutional Neural Network with multi-task objectives: object detection and scene classification in one unified architecture. The proposed network reasons globally about an image to understand the scene, hypothesize object locations, and encodes global scene features with regional object features to improve object recognition. We evaluate our network on the standard SUN RGBD dataset. Experiments show that our approach outperforms state-of-the-arts. Network predictions are further transformed into continuous robot beliefs to ensure temporal coherence and extended to 3D space for robotics applications. We embed the whole framework in Robot Operating System, and evaluate its performance on a real robot for semantic mapping and grasp detection.

We focus on the task of amodal 3D object detection, which is to predict object locations, dimensions, poses and categories in the real world. We introduce a 3D Convolutional Neural Network that takes a volumetric representation of an indoor scene as input and predicts 3D object bounding boxes, object categories, and orientations. Unlike prior state-of-the-arts, our approach does not depend on region proposal techniques to hypothesize object locations. We treat detection and recognition as one regression problem in a single network. Our elegant model is extremely fast and all predictions are reasoned from the global context of a point cloud in a continuous pipeline. We evaluate our approach on two standard datasets: the NYUv2 RGBD dataset and the SUN RGBD dataset. Experiments show that our approach is faster than start-of-the-art 3D detectors by several orders of magnitude towards real-time amodal 3D object detection.