Vision and Navigation for the Carnegie-Mellon Navlab

By C. Thorpe, Martial Hebert, Takeo Kanade, S. Shafer. 1987

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Citations in the corpus

Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation (2017, methodology): “Although methods based on geometric reconstruction and mapping have proven effective in a range of navigation and collision avoidance domains [1], [2], [3], they impose considerable computational overhead [4] and”
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System (2020, background): “While this approach has produced excellent results in a range of settings, from indoor navigation [2] to autonomous driving [3], open-world mobile robot navigation often present challenges that are difficult to address with a purely geometric view of the world.”
Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation (2018, background): “There has been extensive work on autonomous robot navigation [16, 17, 18].”
A survey of video processing techniques for traffic applications (2003, methodology): “Gaussian distributions have been used to model the color classes [11].”
Autonomous driving in urban environments: approaches, lessons and challenges (2010, methodology): “…driving has a rich history, with early demonstrations in the 1980s and 1990s showing the viability of building cars that can control their own motion in complex environments such as highways and urban streets (see Dickmanns (2002) and Thorpe et al. (1988) for excellent reviews of this early work).”
Off-Road Obstacle Avoidance through End-to-End Learning (2005, background): “Such methods have been used successfully for many years for navigation in indoor environments where edge features can be reliably detected and matched [1], but navigation in outdoors environment, despite a long history, is still a challenge [14, 3]: real-time stereo algorithms are considerably less ”
Vision for Mobile Robot Navigation: A Survey (2002, methodology): “Equally impressive progress has been achieved in computer vision for outdoor robotics, as represented by the NAVLAB system [142], [143], [124], [146], [126], [128], the work on vision-guided road-following for " Autobahns " [36], [37], [35], [34], and the Prometheus system [46], [47], [48], [50],…”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “By 1988, Carnegie Mellon’s NAVLAB vehicle was being demonstrated to perform lane-following using camera images [8].”
On-road vehicle detection: a review (2006, methodology): “The latest model in the Navlab family is the Navlab 11, a robot Jeep Wrangler equipped with a wide variety of sensors for short-range and midrange obstacle detection [22], [23], [20].”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, methodology): “Keywords: Computer Vision, Autonomous Vehicles, Autonomous Vision Since the first successful demonstrations in the 1980s (Dickmanns & Mysliwetz (1992); Dickmanns & Graefe (1988); Thorpe et al. (1988)), great progress has been made in the field of autonomous vehicles.”
RoboTHOR: An Open Simulation-to-Real Embodied AI Platform (2020, background): “Spatial navigation approaches [62, 17, 64, 23, 25, 56, 79, 49, 30, 14, 10] typically address navigating towards a pre-specified coordinate or a frame of a scene and they focus on understanding the geometry of the scene and learning better exploration strategies.”