Abstract
Achieving accurate and reliable positioning in dynamic urban scenarios using low-cost vehicular onboard sensors, such as the global navigation satellite systems (GNSS), camera, and inertial measurement unit (IMU), is still a challenging problem. Multi-Agent collaborative integration (MCI) opens a new window for achieving this goal, by sharing the sensor measurements between multiple agents to further improve the accuracy of respective positioning. One of the major difficulties in MCI is to effectively connect all the sensor measurements arising from multiple independent agents. The popular approach is to find the overlapping areas between agents using active sensors, such as cameras. However, the performance of overlapping area detection is significantly degraded in outdoor urban areas due to the challenges arising from numerous unexpected moving objects and unstable illumination conditions. To fill this gap, this paper proposes to leverage both the camera-based overlapping area detection and the inter-ranging measurements to boost the cross-connection between multi-agents and brings the MCI to outdoor urban scenarios using low-cost onboard sensors. Moreover, a novel MCI framework is proposed to integrate the sensor measurements from the low-cost GNSS receiver, camera, IMU, and inter-ranging using state-of-the-art factor graph optimization (FGO) to fully explore their complementary properties. The proposed MCI framework is validated using two challenging datasets collected in urban canyons of Hong Kong. We conclude that the proposed MCI framework can effectively improve the positioning accuracy of the respective agents in the evaluated datasets. We believe that the proposed MCI framework has the potential to be prevalently adopted by the connected intelligent transportation systems (ITS) applications to provide robust positioning using low-cost onboard sensors in urban scenarios.
Highlights
Robust, cost-effective, and accurate positioning is significant for the extensive commercialization of the emerging intelligent transportation systems (ITS) [1] with navigation requirements, such as advanced collision warning systems [2], speed advisory systems [3] and lane reservation systems [4]
Inspired by the work in [21] and [34], this paper proposes to leverage both the vision-based overlapping area detection and the inter-ranging measurements to boost the cross-connection between multi-agents and bring the multi-agents collaborative integration (MCI) to outdoor urban scenarios using low-cost onboard sensors
The major contributions of this paper are listed as follows: (1) This paper proposes to leverage both the vision-based overlapping area detection and the inter-ranging measurements to boost the cross-connection between multi-agents and bring the MCI to outdoor challenging urban scenarios using low-cost onboard vehicular sensors
Summary
Cost-effective, and accurate positioning is significant for the extensive commercialization of the emerging intelligent transportation systems (ITS) [1] with navigation requirements, such as advanced collision warning systems [2], speed advisory systems [3] and lane reservation systems [4]. Inspired by the work in [21] and [34], this paper proposes to leverage both the vision-based overlapping area detection and the inter-ranging measurements to boost the cross-connection between multi-agents and bring the MCI to outdoor urban scenarios using low-cost onboard sensors. A novel MCI framework is proposed to integrate the sensor measurements from low-cost GNSS receiver, camera, IMU, and inter-ranging of each agent, using state-of-the-art factor graph optimization (FGO) to fully release the potential of MCI based on onboard low-cost sensors. To the best of the authors’ knowledge, this is the first paper to employ both the overlapping area detection and inter-ranging to connect multiple agents to achieve the globally referenced collaborative positioning in urban canyons based on low-cost onboard sensors. We believe that the proposed framework can have a positive impact on both the academic and industrial fields
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