RSS 2015 论文列表

Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), do not output a sequence of video frames like standard cameras, but a stream of asynchronous events. An event is triggered when a pixel detects a change of brightness in the scene. An event contains the location, sign, and precise timestamp of the change. The high dynamic range and temporal resolution of the DVS, which is in the order of micro-seconds, make this a very promising sensor for high-speed applications, such as robotics and wearable computing. However, due to the fundamentally different structure of the sensor's output, new algorithms that exploit the high temporal resolution and the asynchronous nature of the sensor are required. In this paper, we address ego-motion estimation for an event-based vision sensor using a continuous-time framework to directly integrate the information conveyed by the sensor. The DVS pose trajectory is approximated by a smooth curve in the space of rigid-body motions using cubic splines and it is optimized according to the observed events. We evaluate our method using datasets acquired from sensor-in-the-loop simulations and onboard a quadrotor performing flips. The results are compared to the ground truth, showing the good performance of the proposed technique.

We describe Chisel : a system for real-time house-scale (300 square meter or more) dense 3D reconstruction onboard a Google Tango mobile device by using a dynamic spatially-hashed truncated signed distance field for mapping, and visual-inertial odometry for localization. By aggressively culling parts of the scene that do not contain surfaces, we avoid needless computation and wasted memory. Even under very noisy conditions, we produce high-quality reconstructions through the use of space carving. We are able to reconstruct and render very large scenes at a resolution of 2-3 cm in real time on a mobile device without the use of GPU computing. The user is able to view and interact with the reconstruction in real-time through an intuitive interface. We provide both qualitative and quantitative results on publicly available RGB-D datasets, and on datasets collected in real-time from two devices.

Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain-Computer Interfaces (BCIs) exacerbates these problems through especially noisy and erratic low-dimensional motion commands due to the difficulty in decoding neural activity. We introduce a general framework to address these challenges through a combination of computer vision, user intent inference, and arbitration between the human input and autonomous control schemes. Adjustable levels of assistance allow the system to balance the operator's capabilities and feelings of comfort and control while compensating for a task's difficulty. We present experimental results demonstrating significant performance improvement using the shared-control assistance framework on adapted rehabilitation benchmarks with two subjects implanted with intracortical brain-computer interfaces controlling a seven degree-of-freedom robotic manipulator as a prosthetic. Our results further indicate that shared assistance mitigates perceived user difficulty and even enables successful performance on previously infeasible tasks. We showcase the extensibility of our architecture with applications to quality-of-life tasks such as opening a door, pouring liquids from containers, and manipulation with novel objects in densely cluttered environments.

Task-Space Inverse Dynamics (TSID) is a well-known optimization-based technique for the control of highly-redundant mechanical systems, such as humanoid robots. One of its main flaws is that it does not take into account any of the uncertainties affecting these systems: poor torque tracking, sensor noises, delays and model uncertainties. As a consequence, the resulting control trajectories may be feasible for the ideal system, but not for the real one. We propose to improve the robustness of TSID by modeling uncertainties in the joint torques as additive white random noise (similarly to LQG). This results in a stochastic optimization problem, in which we can maximize the probability to satisfy the inequality constraints (i.e. to be feasible). Since computing this probability is computationally expensive, we propose three ways to approximate it that are much faster to compute and that we can then use for online control (resolution time below 1 ms). Simulation results show that taking robustness into account greatly increases the chances to have feasible control trajectories (even when the uncertainties affecting the system are not the one modeled in the controller).

Handovers of objects are critical interactions that frequently arise in physical collaborations. In such interactions, humans naturally monitor the pace and workload of their partners and adapt their handovers accordingly. In this paper, we investigate how robots designed to engage in physical collaborations may achieve similar adaptivity in performing handovers. To that end, we collected and analyzed data from human dyads performing a common household task unloading a dish rack where receivers had different levels of task demands. We identified two coordination strategies that enabled givers to adapt to receivers task demands. We then formulated and implemented these strategies on a robotic manipulator. The implemented autonomous system was evaluated in a human-robot interaction study against two baselines that use proactive and reactive coordination methods. The results show a tradeoff between team performance and user experience when human receivers had greater task demands. In particular, the proactive method provided the greatest levels of team performance but offered the poorest user experience compared to the reactive and adaptive methods. The reactive method, while improving user experience over the proactive method, resulted in the poorest team performance. Our adaptive method maintained this improved user experience while offering an improved team performance compared to the reactive method. Our findings offer insights into the tradeoffs involved in the use of these methods and inform the future design of handover interactions for robots.

In this paper, we present a square-root inverse sliding window filter (SR-ISWF) for vision-aided inertial navigation systems (VINS). While regular inverse filters suffer from numerical issues, employing their square-root equivalent enables the usage of single-precision number representations, thus achieving considerable speed ups as compared to double-precision alternatives on resource-constrained mobile platforms. Besides a detailed description of the SR-ISWF for VINS, which focuses on the numerical procedures that enable exploiting the problem's structure for gaining in efficiency, this paper presents a thorough validation of the algorithm's processing requirements and achieved accuracy. In particular, experiments are conducted using a commercial-grade cell phone, where the proposed algorithm is shown to achieve the same level of estimation accuracy, when compared to state-of-the-art VINS algorithms, with significantly higher speed.

We consider an optimal stopping formulation of the mission monitoring problem, where a monitor vehicle must remain in close proximity to an autonomous robot that stochastically follows a pre-planned trajectory. This problem arises when autonomous underwater vehicles are monitored by surface vessels, and in a diverse range of other scenarios. The key problem characteristics we consider are that the monitor must remain stationary while observing the robot, and that the robot motion is modelled in general as a stochastic process. We propose a resolution-complete algorithm for this problem that runs in polynomial time. The algorithm is based on a sweep-plane approach and generates a motion plan that maximises the expected observation time. A variety of stochastic models may be used to represent the expected robot trajectory. We present results drawn from real AUV trajectories and Monte Carlo simulations that validate the correctness of our algorithm and its feasibility in practice.

The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filter. GFs represent the belief of the current state by a Gaussian with the mean being an affine function of the measurement. We show that this representation can be too restrictive to accurately capture the dependences in systems with nonlinear observation models, and we investigate how the GF can be generalized to alleviate this problem. To this end, we view the GF from a variational-inference perspective. We analyse how restrictions on the form of the belief can be relaxed while maintaining simplicity and efficiency. This analysis provides a basis for generalizations of the GF. We propose one such generalization which coincides with a GF using a virtual measurement, obtained by applying a nonlinear function to the actual measurement. Numerical experiments show that the proposed Feature Gaussian Filter (FGF) can have a substantial performance advantage over the standard GF for systems with nonlinear observation models.