Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance

TL;DR

This paper introduces an online collision avoidance system combining visual-inertial data, object detection, and recurrent neural networks within an MPC framework to enhance safe navigation in cluttered environments.

Contribution

It presents a novel integration of RNN-based covariance inference with visual-inertial odometry and object detection for risk-averse MPC in robotics.

Findings

Validated on complex quadruped robot dynamics

Demonstrated fast, collision-free path planning

Applicable to various robotic platforms

Abstract

In this paper, we propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties for safer navigation through cluttered environments. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates through each step of our MPC's finite time horizon. The RNN model is trained on a dataset that comprises of robot and landmark poses generated from camera images and inertial measurement unit (IMU) readings via a state-of-the-art visual-inertial odometry framework. To detect and extract object locations for avoidance, we use a custom-trained convolutional neural network model in conjunction with a feature extractor to retrieve 3D centroid and radii boundaries of nearby obstacles. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms, demonstrating autonomous behaviors that can plan fast and collision-free paths towards a goal point.