X-band radar based SLAM in Singapore's off-shore environment

Abstract

This paper presents a simultaneous localisation and mapping (SLAM) algorithm implemented on an autonomous sea kayak with a commercial off-the-shelf X-band marine radar mounted. The Autonomous Surface Craft (ASC) was driven in an off-shore test site in Singapore's southern Selat Puah marine environment. Data from the radar, GPS and an inexpensive singleaxis gyro data were logged by an on-board processing unit as the ASC traversed the environment, which comprised geographical and surface vessel landmarks. An automated feature extraction routine is presented, based on a probabilistic landmark detector, followed by a clustering and centroid approximation approach. With restrictive feature modeling, and a lack of vehicle control input information, it is demonstrated that via the novel RB-PHD-SLAM Filter, useful results can be obtained, despite an actively rolling and pitching ASC on the sea surface. In addition, the merits of investigating ASC SLAM are demonstrated, particularly with respect to the map estimation, obstacle avoidance and target tracking problems. Despite the presence of GPS and gyro data, heading information on such small ASC's is greatly compromised which induces large sensing error, further accentuate by the large range of the radar sensor. This work is a step towards realising an ASC capable of performing environmental or security surveillance and reporting a real-time active awareness of the above-water scene.

A novel positioning approach for an intelligent vessel based on an improved simultaneous localization and mapping algorithm and marine radar

“…When the offshore radar images acquired in real time match the electronic chart, the position of the USV could be determined. For mapping the offshore chart of Singapore, Mullane et al 16 used the image clustering method and calculated the graphical centroid.…”

Inertial Navigation and Mapping for Autonomous Vehicles

“…Table 1.1: Some SLAM publications using various platforms and sensors. 1 (Karlsson et al, 2008;Caballero et al, 2009;Bryson and Sukkarieh, 2009;Sukkarieh, 2009, 2008;Bryson and Sukkarieh, 2008), 2 (Wang and Dissanayake, 2012;Thrun and Montemerlo, 2006;Callmer et al, 2008), 3 (Davison et al, 2007;Davison, 2003;Eade, 2008;Klein and Murray, 2007), 4 (Callmer et al, 2011), 5 (Kim, 2013), 6 (Jung and Lacroix, 2003), 7 (Konolige and Agrawal, 2008Cummins andNewman, 2010), 8 (Lupton andSukkarieh, 2012;Karlsson and Bjärkefur, 2010;Strasdat et al, 2011;Taylor, 2001), 9 (Eustice et al, 2006;Mahon et al, 2008), 10 (Sjanic and, 11 (Marck et al;Gerossier et al, 2009), -(), 12 (Callmer et al, 2011;Mullane et al, 2010), 13 (Choi et al, 2005), -(), 14 (Wrobel, 2014), 15 (Newman et al, 2003;Ribas et al, 2006), 16 (Bryson et al, 2010), 17 , 18 (Lupton and Sukkarieh, 2012), 19 (Han and Kim, 2013), 20 Eustice, 2009), 21 (Fossel et al, 2013), 22 (Bosse and Zlot, 2008), 23 (Han and Kim, 2013). Paper A presents a method for localisation of underwater sensors equipped with triaxial magnetometers using a friendly vessel with known magnetic characteristics.…”

Towards adaptive sensor fusion for simultaneous localization and mapping

“…Most SLAM algorithms are formulated for a single autonomous platform using observations from a single exteroceptive sensor. SLAM algorithms maneuvering in a single plane (such as ground vehicles [16], [54], [18], [11], [13], [70], [67] and [52], or marine platforms [51]), employ a 3 Degree of Freedom (3 DOF) vehicle state containing the Cartesian coordinates of the vehicle, (x, y), along with the vehicle orientation θ .…”

“…• Feature based maps: SLAM formulations employ a wide spectrum of possible environmental landmarks depending on the application scenario. For example, bushes or trees for outdoor ground vehicles [26], wall corners for indoor ground vehicles navigating in a plane [43], or buoys or stationary ships in marine vehicles [51], may all be viable landmarks for SLAM.…”

“…The subsequent search for adaptive state estimation algorithms displaying greater flexibility in formulation resulted in the Adaptive Limited Memory Filter (ALMF) algorithm, described in the next section.3.3 Adaptive Limited Memory Filter for Nonlinear Models39 3.3 Adaptive Limited Memory Filter for Nonlinear ModelsThe Adaptive Limited Memory Filter (ALMF)[53] was formulated for the standard Kalman Filter, and hence was affected by the issues arising from the assumptions used in its formulation. It was limited to linear process and noise models, which prohibited its use in most real world scenarios[51] dealing with nonlinear models, including vehicle localization and SLAM.…”