Mirko Nava

Simone Arreghini, Nicholas Carlotti, Mirko Nava et al.

Localizing humans is a key prerequisite for any service robot operating in proximity to people. In these scenarios, robots rely on a multitude of state-of-the-art detectors usually designed to operate with RGB-D cameras or expensive 3D LiDARs. However, most commercially available service robots are equipped with cameras with a narrow field of view, making them blind when a user is approaching from other directions, or inexpensive 1D LiDARs whose readings are difficult to interpret. To address these limitations, we propose a self-supervised approach to detect humans and estimate their 2D pose from 1D LiDAR data, using detections from an RGB-D camera as a supervision source. Our approach aims to provide service robots with spatial awareness of nearby humans. After training on 70 minutes of data autonomously collected in two environments, our model is capable of detecting humans omnidirectionally from 1D LiDAR data in a novel environment, with 71% precision and 80% recall, while retaining an average absolute error of 13 cm in distance and 44° in orientation.

Mirko Nava, Antonio Paolillo, Jérôme Guzzi et al.

We propose a general self-supervised learning approach for spatial perception tasks, such as estimating the pose of an object relative to the robot, from onboard sensor readings. The model is learned from training episodes, by relying on: a continuous state estimate, possibly inaccurate and affected by odometry drift; and a detector, that sporadically provides supervision about the target pose. We demonstrate the general approach in three different concrete scenarios: a simulated robot arm that visually estimates the pose of an object of interest; a small differential drive robot using 7 infrared sensors to localize a nearby wall; an omnidirectional mobile robot that localizes itself in an environment from camera images. Quantitative results show that the approach works well in all three scenarios, and that explicitly accounting for uncertainty yields statistically significant performance improvements.

Mirko Nava, Jerome Guzzi, R. Omar Chavez-Garcia et al.

We introduce a general self-supervised approach to predict the future outputs of a short-range sensor (such as a proximity sensor) given the current outputs of a long-range sensor (such as a camera); we assume that the former is directly related to some piece of information to be perceived (such as the presence of an obstacle in a given position), whereas the latter is information-rich but hard to interpret directly. We instantiate and implement the approach on a small mobile robot to detect obstacles at various distances using the video stream of the robot's forward-pointing camera, by training a convolutional neural network on automatically-acquired datasets. We quantitatively evaluate the quality of the predictions on unseen scenarios, qualitatively evaluate robustness to different operating conditions, and demonstrate usage as the sole input of an obstacle-avoidance controller. We additionally instantiate the approach on a different simulated scenario with complementary characteristics, to exemplify the generality of our contribution.