Guided Direct Time-of-Flight Lidar Using Stereo Cameras for Enhanced Laser Power Efficiency

Author:

Taneski Filip1ORCID,Gyongy Istvan1ORCID,Al Abbas Tarek2ORCID,Henderson Robert K.1ORCID

Affiliation:

1. Institute for Integrated Micro and Nano Systems, University of Edinburgh, Edinburgh EH9 3FF, UK

2. Ouster Automotive, Ouster, Inc., Edinburgh EH2 4AD, UK

Abstract

Self-driving vehicles demand efficient and reliable depth-sensing technologies. Lidar, with its capability for long-distance, high-precision measurement, is a crucial component in this pursuit. However, conventional mechanical scanning implementations suffer from reliability, cost, and frame rate limitations. Solid-state lidar solutions have emerged as a promising alternative, but the vast amount of photon data processed and stored using conventional direct time-of-flight (dToF) prevents long-distance sensing unless power-intensive partial histogram approaches are used. In this paper, we introduce a groundbreaking ‘guided’ dToF approach, harnessing external guidance from other onboard sensors to narrow down the depth search space for a power and data-efficient solution. This approach centers around a dToF sensor in which the exposed time window of independent pixels can be dynamically adjusted. We utilize a 64-by-32 macropixel dToF sensor and a pair of vision cameras to provide the guiding depth estimates. Our demonstrator captures a dynamic outdoor scene at 3 fps with distances up to 75 m. Compared to a conventional full histogram approach, on-chip data is reduced by over twenty times, while the total laser cycles in each frame are reduced by at least six times compared to any partial histogram approach. The capability of guided dToF to mitigate multipath reflections is also demonstrated. For self-driving vehicles where a wealth of sensor data is already available, guided dToF opens new possibilities for efficient solid-state lidar.

Funder

Ouster Inc.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference39 articles.

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3. Aptiv, A., Apollo, B., Continenta, D., FCA, H., and Infineon, I.V. (2023, October 28). Safety First For Automated Driving [White Paper]. Available online: https://group.mercedes-benz.com/documents/innovation/other/safety-first-for-automated-driving.pdf.

4. Ford (2018). A Matter of Trust: Ford’s Approach to Developing Self-Driving Vehicles, Ford.

5. Performance Analysis of 10 Models of 3D LiDARs for Automated Driving;Lambert;IEEE Access,2020

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