LAUSR

Precise relative positioning has numerous applications in collision avoidance and lane-keeping/lane-changing maneuvers. For such applications, we need sensors that can precisely estimate the relative position. GPS carrier phase measurements can provide high accuracy if unknown cycle ambiguities (integers) are resolved correctly from among an integer search space of potential candidates. However, resolving these integers becomes challenging in urban areas where tall buildings and tree canopy can lead to poor satellite visibility and degraded measurement quality. To aid with integer fixing, multiple UWBs can be utilized, which provide noisy inter-ranging measurements at a high sampling frequency. We propose a precise relative positioning framework for a two-car setup via tight coupling of GPS carrier-phase and inter-ranging measurements from multiple UWBs. With these range measurements, we form an a priori estimate of the relative position that adaptively constrains the position search space and subsequently reduces the induced integer search space. Adaptively constraining the integer search space improves integer fixing rate, enabling precise relative positioning. We demonstrate our algorithms performance on a real-world dataset collected with two cars at Stanford University. Our algorithm achieves improved accuracy over baseline methods, even in challenging urban settings.