A material handling method comprising: generating, by an image processing unit, three-dimensional, 3-D point cloud data (300, 400) based on a field-of-view, FOV, of an image capturing unit, wherein the 3-D point cloud data comprises a plurality of points representing an object in the FOV, and wherein each of the plurality of points includes a coordinate of the point and orientation data pertaining to an orientation of the represented object with respect to a material handling apparatus, wherein the FOV comprises an interior of a container including a product wall, a floor, a ceiling, sidewalls, and a back wall; at the image processing unit, segmenting the 3-D point cloud data to obtain a plurality of cluster areas, being subsets of points characterized by having orientation data within a defined range of one another; extracting, by the image processing unit, a first set of cluster areas from the plurality of cluster areas based on orientation data associated with the plurality of cluster areas, the first set of cluster areas corresponding to one or more from among the product wall, the floor, the ceiling, and the back wall; characterized by: determining, by the image processing unit, a candidate region of the first set of cluster areas, wherein the candidate region comprises a cluster area having a ratio of cross-sectional area of the cluster area and a cross-sectional area of the container that exceeds a first cross-sectional threshold; generating, by a programmable logic controller, a two-dimensional depth map comprising the candidate region; classifying, by a machine learning unit, the candidate region as one of the back wall of the container and the product wall; and in the case that the candidate region is classified as the back wall of the container, defining, by a processor, a navigation path in the container based on a data set associated with the back wall of the container, wherein the navigation path is configured to prevent collision with the back wall of the container. generating, by an image processing unit, three-dimensional, 3-D point cloud data (300, 400) based on a field-of-view, FOV, of an image capturing unit, wherein the 3-D point cloud data comprises a plurality of points representing an object in the FOV, and wherein each of the plurality of points includes a coordinate of the point and orientation data pertaining to an orientation of the represented object with respect to a material handling apparatus, wherein the FOV comprises an interior of a container including a product wall, a floor, a ceiling, sidewalls, and a back wall; at the image processing unit, segmenting the 3-D point cloud data to obtain a plurality of cluster areas, being subsets of points characterized by having orientation data within a defined range of one another; extracting, by the image processing unit, a first set of cluster areas from the plurality of cluster areas based on orientation data associated with the plurality of cluster areas, the first set of cluster areas corresponding to one or more from among the product wall, the floor, the ceiling, and the back wall; characterized by: determining, by the image processing unit, a candidate region of the first set of cluster areas, wherein the candidate region comprises a cluster area having a ratio of cross-sectional area of the cluster area and a cross-sectional area of the container that exceeds a first cross-sectional threshold; generating, by a programmable logic controller, a two-dimensional depth map comprising the candidate region; classifying, by a machine learning unit, the candidate region as one of the back wall of the container and the product wall; and in the case that the candidate region is classified as the back wall of the container, defining, by a processor, a navigation path in the container based on a data set associated with the back wall of the container, wherein the navigation path is configured to prevent collision with the back wall of the container. determining, by the image processing unit, a candidate region of the first set of cluster areas, wherein the candidate region comprises a cluster area having a ratio of cross-sectional area of the cluster area and a cross-sectional area of the container that exceeds a first cross-sectional threshold; generating, by a programmable logic controller, a two-dimensional depth map comprising the candidate region; classifying, by a machine learning unit, the candidate region as one of the back wall of the container and the product wall; and in the case that the candidate region is classified as the back wall of the container, defining, by a processor, a navigation path in the container based on a data set associated with the back wall of the container, wherein the navigation path is configured to prevent collision with the back wall of the container.