Proprioceptive localization refers to a new class of robot egocentric localization methods that do not rely on the perception and recognition of external landmarks. These methods are naturally immune to bad weather, poor lighting conditions, or other extreme environmental conditions that may hinder exteroceptive sensors such as a camera or a laser ranger finder. These methods depend on proprioceptive sensors such as inertial measurement units and/or wheel encoders. Assisted by magnetoreception, the sensors can provide a rudimentary estimation of vehicle trajectory which is used to query a prior known map to obtain location. Named as graph-based proprioceptive localization, we provide a low cost fallback solution for localization under challenging environmental conditions. As a robot/vehicle travels, we extract a sequence of heading-length values for straight segments from the trajectory and match the sequence with a preprocessed heading-length graph (HLG) abstracted from the prior known map to localize the robot under a graph-matching approach. Using the information from HLG, our location alignment and verification module compensates for trajectory drift, wheel slip, or tire inflation level. We have implemented our algorithm and tested it in both simulated and physical experiments. The algorithm runs successfully in finding robot location continuously and achieves localization accurate at the level that the prior map allows (less than 10 m).

Existing autonomous vehicle (AV) navigation algorithms treat lane recognition, obstacle avoidance, local path planning, and lane following as separate functional modules which result in driving behavior that is incompatible with human drivers. It is imperative to design human-compatible navigation algorithms to ensure transportation safety. We develop a new tightly-coupled perception-planning framework that combines all these functionalities to ensure human-compatibility. Using GPS-camera-lidar sensor fusion, we detect actual lane boundaries (ALBs) and propose availability-reasonability-feasibility (ARF) threefold tests to determine if we should generate virtual lane boundaries (VLBs) or follow ALBs. If needed, VLBs are generated using a dynamically adjustable multi-objective optimization framework that considers obstacle avoidance, trajectory smoothness (to satisfy vehicle kinodynamic constraints), trajectory continuity (to avoid sudden movements), GPS following quality (to execute global plan), and lane following or partial direction following (to meeting human expectation). Consequently, vehicle motion is more human compatible than existing approaches. We have implemented our algorithm and tested under open source data with satisfying results.

In this paper, we focus on recovering a 3-D depth map from a single image. Given an image of urban scene, we extract linear perspective information to establish the 3-D scene model. Unlike approaches which use only occlusion relationship between objects to estimate the relative depth of the image, we further combine the perspective geometry information with the occlusion relationship between objects. Besides, we propose the construction of depth gradient maps to represent the depth variation trend along the vertical and horizontal directions. The image is first partitioned into geometric components and initial depth gradient maps are generated based on the relative position between the vanishing point and the classified components. Incorporating main directions of vanishing lines and occlusion boundaries in the initial depth gradient maps, a refined depth map is obtained by using a CRF (conditional random field) model. We demonstrate that our approach can produce realistic relative depth maps for images of urban scenes.