CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a filing under 35 U.S.C. 371 as the National Stage of International Application No. PCT/SG2017/050446, filed Sep. 7, 2017, entitled “SIMULTANEOUS LOCALIZATION AND MAPPING METHODS AND APPARATUS,” which claims priority to Singapore Application No. SG 10201607539W filed with the Intellectual Property Office of Singapore on Sep. 9, 2016, both of which are incorporated herein by reference in their entirety for all purposes.

FIELD OF THE INVENTION

The present disclosure relates to simultaneous localization and mapping (SLAM). In particular, it relates to SLAM methods and systems with reduced computational complexity.

BACKGROUND OF THE INVENTION

Self-localization and mapping is one of the most basic capabilities of autonomous robots, and has received increasing attention in tandem with the development of hardware, such as smarter sensors and faster processors. However, 3-D Mapping algorithms are difficult to apply directly to micro-robot systems, especially Micro Unmanned Aerial Vehicles (MUAV) due to the following reasons. First, different from ground robots, MUAV are unable to provide odometer information to correct unacceptable incremental drift in GPS-denied environment. Second, micro-robot systems can only provide limited computational resources, such as ultra-low power embedded processors, due to payload and power limitations. Third, dense maps, which are crucial for higher-level applications, such as collision-free motion planning, object detection and scene understanding, require more computational resources to produce. The motivation of the present disclosure is to develop a light 3-D dense mapping system that can be carried out by micro-robot systems, and achieves faster computational speed with sufficient accuracy.

In the simultaneous localization and mapping (SLAM) problem, data association, which is to relate the sensors' measurements with the landmarks within maps, is one of the most important and time-consuming parts.

In existing SLAM methods, iterative solutions play an important role and have become the bottleneck.

SUMMARY OF THE INVENTION

A simultaneous localization and mapping method according to a first aspect of the present disclosure comprises: receiving a frame of three dimensional point cloud data from a three dimensional point cloud sensor coupled to a robot; receiving inertial measurement data indicative of a change in pose of the three dimensional point cloud sensor; using the inertial measurement data to estimate a rotation between the received frame of three dimensional point cloud data and a key frame of three dimensional point cloud data; applying a rotation to the received frame of three dimensional point cloud data to obtain an aligned frame of three dimensional point cloud data, the aligned frame of three dimensional point cloud data having an orientation aligned with the key frame of three dimensional point cloud data; and estimating a translation of the aligned frame of three dimensional point cloud data from the key frame of three dimensional point cloud data.

By rotating the three dimensional point cloud to align it with the key frame, the process of estimating a translation of the three dimensional point cloud data can be decoupled to spaces with lower degrees of freedom which reduces the computational complexity of the SLAM processing.

In an embodiment, the method further comprises applying an axonornetric projection to the aligned frame of three dimensional point cloud data to obtain axonornetric image frame data and wherein translation of the aligned frame of three dimensional point cloud data from the key frame of three dimensional point cloud data comprises estimating a translation of the axonornetric image frame data in an axonometric image plane.

In an embodiment the method further comprises expanding the axonornetric image frame data into a one-dimensional vector and wherein estimating a translation of the axonometric image frame data in an axonornetric image plane comprises determining a cyclic shift of the one-dimensional vector.

The estimation of the translation of the aligned frame of three dimensional point cloud data from the key frame of three dimensional point cloud data may be carried out by applying a correlation filter. In some embodiments, the correlation filter is a general kemelized correlation filter (GKCF). The correlation filter may also be implemented as a kernelized correlation filter (KCF).

The method may comprise creating a new key frame if a matching criterion is below a first threshold. The method may further comprise refining the key frame using the aligned frame of three dimensional point cloud data if the matching criterion is above a second threshold. In some embodiments the second threshold is greater than the first threshold. The first matching criterion and/or the second matching criterion may be a peak to side-lobe ratio. In some embodiments the method further comprises refining the key frame using the aligned frame of three dimensional point cloud data.

According to a second aspect of the present disclosure a simultaneous localization and mapping system is provided. The system comprises: a three dimensional point cloud sensor; an inertial sensor; and a processing module configured to: receive a frame of three dimensional point cloud data from the three dimensional point cloud sensor; receive inertial measurement data indicative of a change in pose of the three dimensional point cloud sensor; use the inertial measurement data to estimate a rotation between the received frame of three dimensional point cloud data and a key frame of three dimensional point cloud data; applying a rotation to the received frame of three dimensional point cloud data to obtain an aligned frame of three dimensional point cloud data, the aligned frame of three dimensional point cloud data having an orientation aligned with the key frame of three dimensional point cloud data; and estimate a translation of the aligned frame of three dimensional point cloud data from the key frame of three dimensional point cloud data.

The three dimensional point cloud sensor may be a RGB-D camera, and/or a Stereo Camera and/or a 3D laser scanner.

