Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization

Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure and loop closure detection. Recent random forests based methods exploit randomly sampled pixel comparison features to predict 3D world locations for 2D image locations to guide the camera pose optimization. However, these image features are only sampled randomly in the images, without considering the spatial structures or geometric information, leading to large errors or failure cases with the existence of poorly textured areas or in motion blur. Line segment features are more robust in these environments. In this work, we propose to jointly exploit points and lines within the framework of uncertainty driven regression forests. The proposed approach is thoroughly evaluated on three publicly available datasets against several strong state-of-the-art baselines in terms of several different error metrics. Experimental results prove the efficacy of our method, showing superior or on-par state-of-the-art performance.Comment: published as a conference paper at 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Energy regeneration from suspension dynamic modes and self-powered actuation

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper concerns energy harvesting from vehicle suspension systems. The generated power associated with bounce, pitch and roll modes of vehicle dynamics is determined through analysis. The potential values of power generation from these three modes are calculated. Next, experiments are carried out using a vehicle with a four jack shaker rig to validate the analytical values of potential power harvest. For the considered vehicle, maximum theoretical power values of 1.1kW, 0.88kW and 0.97kW are associated with the bounce, pitch and roll modes, respectively, at 20 Hz excitation frequency and peak to peak displacement amplitude of 5 mm at each wheel, as applied by the shaker. The corresponding experimentally power values are 0.98kW, 0.74kW and 0.78kW. An experimental rig is also developed to study the behavior of regenerative actuators in generating electrical power from kinetic energy. This rig represents a quarter-vehicle suspension model where the viscous damper in the shock absorber system is replaced by a regenerative system. The rig is able to demonstrate the actual electrical power that can be harvested using a regenerative system. The concept of self-powered actuation using the harvested energy from suspension is discussed with regard to applications of self-powered vibration control. The effect of suspension energy regeneration on ride comfort and road handling is presented in conjunction with energy harvesting associated with random road excitations.Peer reviewedFinal Accepted Versio