Binary TTC: A Temporal Geofence for Autonomous Navigation

被引:20
|
作者
Badki, Abhishek [1 ,2 ]
Gallo, Orazio [1 ]
Kautz, Jan [1 ]
Sen, Pradeep [2 ]
机构
[1] NVIDIA, Santa Clara, CA 95051 USA
[2] UC Santa Barbara, Santa Barbara, CA 93106 USA
关键词
D O I
10.1109/CVPR46437.2021.01275
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Time-to-contact (TTC), the time for an object to collide with the observer's plane, is a powerful tool for path planning: it is potentially more informative than the depth, velocity, and acceleration of objects in the scene-even for humans. TTC presents several advantages, including requiring only a monocular, uncalibrated camera. However, regressing TTC for each pixel is not straightforward, and most existing methods make over-simplifying assumptions about the scene. We address this challenge by estimating TTC via a series of simpler, binary classifications. We predict with low latency whether the observer will collide with an obstacle within a certain time, which is often more critical than knowing exact, per-pixel TTC. For such scenarios, our method offers a temporal geofence in 6.4 ms-over 25x faster than existing methods. Our approach can also estimate per-pixel TTC with arbitrarily fine quantization (including continuous values), when the computational budget allows for it. To the best of our knowledge, our method is the first to offer TTC information (binary or coarsely quantized) at sufficiently high frame-rates for practical use.
引用
收藏
页码:12941 / 12950
页数:10
相关论文
共 50 条
  • [31] Autonomous underwater navigation and control
    Williams, SB
    Newman, P
    Rosenblatt, J
    Dissanayake, G
    Durrant-Whyte, H
    ROBOTICA, 2001, 19 : 481 - 496
  • [32] Autonomous and advanced navigation techniques
    Regnier, P
    Vaillon, L
    Strandmoe, S
    4TH ESA INTERNATIONAL CONFERENCE ON SPACECRAFT GUIDANCE, NAVIGATION AND CONTROL SYSTEMS AND TUTORIAL ON MODERN AND ROBUST CONTROL: THEORY, TOOLS AND APPLICATIONS, 2000, 425 : 523 - 528
  • [33] Mars Rover Autonomous Navigation
    M. Maurette
    Autonomous Robots, 2003, 14 : 199 - 208
  • [34] AN AUTONOMOUS VEHICLE NAVIGATION ALGORITHM
    MITCHELL, JSB
    PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 1984, 485 : 153 - 158
  • [35] Autonomous Navigation of UAV in Forest
    Cui, Jin Qiang
    Lai, Shupeng
    Dong, Xiangxu
    Liu, Peidong
    Chen, Ben M.
    Lee, Tong H.
    2014 INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS (ICUAS), 2014, : 726 - 733
  • [36] AUTONOMOUS OPTICAL LUNAR NAVIGATION
    Crouse, Brian
    Zanetti, Renato
    D'Souza, Chris
    Spanos, Pol D.
    SPACEFLIGHT MECHANICS 2009, VOL 134, PTS I-III, 2009, 134 : 327 - +
  • [37] Sensing for Autonomous Vehicle Navigation
    Zhang, Wende
    2009 CONFERENCE ON LASERS AND ELECTRO-OPTICS AND QUANTUM ELECTRONICS AND LASER SCIENCE CONFERENCE (CLEO/QELS 2009), VOLS 1-5, 2009, : 3240 - 3241
  • [38] Adaptive navigation for autonomous robots
    Knudson, Matt
    Tumer, Kagan
    ROBOTICS AND AUTONOMOUS SYSTEMS, 2011, 59 (06) : 410 - 420
  • [39] Predictive autonomous robot navigation
    Foka, AF
    Trahanias, PE
    2002 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-3, PROCEEDINGS, 2002, : 490 - 495
  • [40] Autonomous Robot Navigation in Crowd
    Afonso, Paulo de Almeida
    Ferreira Jr, Paulo Roberto
    2022 LATIN AMERICAN ROBOTICS SYMPOSIUM (LARS), 2022 BRAZILIAN SYMPOSIUM ON ROBOTICS (SBR), AND 2022 WORKSHOP ON ROBOTICS IN EDUCATION (WRE), 2022, : 139 - 144